Genome-wide tracing to decipher nuclear organization

Nuclear organization impacts gene expression activity and cell phenotype. Our current understanding is mainly derived from ensemble-level sequencing studies that reflect the 3D genome structure of millions of cells. These approaches have provided invaluable details on the 3D organizations of the genome and their relation to other nuclear landmarks. However, they mostly lack the ability to provide multimodal information simultaneously at the single-cell level. In recent years, cutting-edge imaging technologies have risen to the challenge of simultaneously describing multiple components of the nuclear space at the single-cell level, paving the way for a deeper understanding of the genome structure–function relationship. This review will focus on the development and utilization of such technologies to gain a multi-component view of the nucleus at single-cell resolution, dissecting the complexity and heterogeneity of nuclear organization.     

单细胞基因组测序技术新进展及其在生物医学中的应用

随着单细胞基因组测序技术的建立与发展,对细胞基因组特征的分析进入了单细胞水平。单细胞的基因组分辨率不但使研究人员能够在单细胞尺度上分析肿瘤细胞的异质性,也使得传统上难以检测的稀有细胞的基因组研究成为可能。这些稀有细胞往往具有重要的生物学意义或临床价值,如癌症患者血液中循环肿瘤细胞(circulatingtumorcell,CTC)的基因组检测或三代试管婴儿植入前胚胎细胞的遗传缺陷诊断与筛查(preimplantation genetic diagnosis/screening, PGD/PGS)。本文总结了近年来发展的各种单细胞基因组扩增技术及其优缺点,并介绍了单细胞基因组测序技术在肿瘤生物学和临床检测中的应用,以期为单细胞基因组测序技术在临床检测中应用开发提供参考。     

Recent advances of single-cell RNA sequencing technology in mesenchymal stem cell research

Mesenchymal stem cells (MSCs) are multipotent stromal cells with great potential for clinical applications. However, little is known about their cell heterogeneity at a single-cell resolution, which severely impedes the development of MSC therapy. In this review, we focus on advances in the identification of novel surface markers and functional subpopulations of MSCs made by single-cell RNA sequencing and discuss their participation in the pathophysiology of stem cells and related diseases. The challenges and future directions of single-cell RNA sequencing in MSCs are also addressed in this review.     

Integrated analysis of multimodal single-cell data with structural similarity

Multimodal single-cell sequencing technologies provide unprecedented information on cellular heterogeneity from multiple layers of genomic readouts. However, joint analysis of two modalities without properly handling the noise often leads to overfitting of one modality by the other and worse clustering results than vanilla single-modality analysis. How to efficiently utilize the extra information from single cell multi-omics to delineate cell states and identify meaningful signal remains as a significant computational challenge. In this work, we propose a deep learning framework, named SAILERX, for efficient, robust, and flexible analysis of multi-modal single-cell data. SAILERX consists of a variational autoencoder with invariant representation learning to correct technical noises from sequencing process, and a multimodal data alignment mechanism to integrate information from different modalities. Instead of performing hard alignment by projecting both modalities to a shared latent space, SAILERX encourages the local structures of two modalities measured by pairwise similarities to be similar. This strategy is more robust against overfitting of noises, which facilitates various downstream analysis such as clustering, imputation, and marker gene detection. Furthermore, the invariant representation learning part enables SAILERX to perform integrative analysis on both multi- and single-modal datasets, making it an applicable and scalable tool for more general scenarios.     

Applying single-cell highly multiplexed secretome proteomics to characterize immunotherapeutic products and predict clinical responses

Genetically and phenotypically identical immune cell populations can be highly heterogenous in terms of their immune functions and protein secretion profiles. The microfluidic chip-based single-cell highly multiplexed secretome proteomics enables characterization of cellular heterogeneity of immune responses at different cellular and molecular layers. Increasing evidence has demonstrated that polyfunctional T cells that simultaneously produce 2+ proteins per cell at the single-cell level are key effector cells that contribute to the development of potent and durable cellular immunity against pathogens and cancers. The functional proteomic technology offers a wide spectrum of cellular function assessment and can uniquely define highly polyfunctional cell subsets with cytokine signatures from live individual cells. This high-dimensional single-cell analysis provides deep dissection into functional heterogeneity and helps identify predictive biomarkers and potential correlates that are crucial for immunotherapeutic product design optimization and personalized immunotherapy development to achieve better clinical outcomes.     

De novo assembly of human genome at single-cell levels

Genome assembly has been benefited from long-read sequencing technologies with higher accuracy and higher continuity. However, most human genome assembly require large amount of DNAs from homogeneous cell lines without keeping cell heterogeneities, since cell heterogeneity could profoundly affect haplotype assembly results. Herein, using single-cell genome long-read sequencing technology (SMOOTH-seq), we have sequenced K562 and HG002 cells on PacBio HiFi and Oxford Nanopore Technologies (ONT) platforms and conducted de novo genome assembly. For the first time, we have completed the human genome assembly with high continuity (with NG50 of ∼2 Mb using 95 individual K562 cells) at single-cell levels, and explored the impact of different assemblers and sequencing strategies on genome assembly. With sequencing data from 30 diploid individual HG002 cells of relatively high genome coverage (average coverage ∼41.7%) on ONT platform, the NG50 can reach over 1.3 Mb. Furthermore, with the assembled genome from K562 single-cell dataset, more complete and accurate set of insertion events and complex structural variations could be identified. This study opened a new chapter on the practice of single-cell genome de novo assembly.     

Methods for predicting single-cell miRNA in breast cancer

It has been demonstrated that miRNAs are involved in many biological processes including cell proliferation and differentiation, apoptosis, and stress responses. Although single-cell RNA sequencing technology is prevailing nowadays, it still remains challenging in quantifying miRNA at the single-cell level. Herein, we present the computational methods to infer the single-cell miRNA expression level using its target gene abundances. Firstly, we developed an enrichment-based approach in estimating miRNA expression considering miRNA-mRNA regulation information and miRNA-mRNA correlation signal captured from existing TCGA datasets. Further efforts were made to infer the miRNA expression with machine learning models. The methods were applied to compare the accuracy and robustness with the simulated single-cell data. Finally, we applied the method in single-cell RNA-seq triple negative breast cancer (TNBC) patients to further discover miRNA marker at the single-cell level for the malignant cells. Our tool is available online at: https://github.com/ChengkuiZhao/Single-cell-miRNA-prediction.     

单细胞转录组测序技术在心脏发育、疾病以及医学中的 应用

单细胞转录组测序(Single-cell RNA sequencing,scRNA-seq)可以在单细胞水平描绘出每个细胞同一基因的表达量在不同细胞间的表达水平差异,使得在单细胞水平重新认识各种组织器官成为可能.目前对心脏的测序研究正从传统的普通转录组水平过渡到单细胞水平,对小鼠和人的心脏的测序陆续地发表出来.概述了s...     

单细胞测序技术在传染病及微生物领域的应用研究进展

单细胞测序技术正逐渐成为生物学基础研究的“必备工具”,为我们理解各种生物学现象带来革命性的洞见。很多传染性疾病均涉及免疫细胞的差异化功能,而这些免疫细胞之间具有较大的异质性。与传统的批量高通量测序相比,近年来新兴的单细胞转录组测序使得研究者能够分析感染过程的免疫细胞异质性,充分挖掘珍贵的临床样本的分子信息,还能获取难以培养的病原微生物的遗传信息。本文着重介绍了当前单细胞测序在传染性疾病及病原微生物研究领域中的应用情况,并对其发展前景做了简要展望。     

CFP荧光蛋白文库构建及FRET技术筛选钙调素结合蛋白

钙调素（Calmodulin,CaM）是细胞内Ca^2＋信号的主要受体,能够与靶蛋白相互结合调节靶蛋白的活性,在细胞增殖、分化、凋亡、迁移等过程中都起着重要作用。荧光共振能量转移（fluorescence resonance energy transfer,FRET）技术是目前研究蛋白质相互作用比较成熟的方法之一。作者通过Cre-loxP位点特异性重组技术构建了带有CFP荧光蛋白标记的文库,与YFP—CaM共同转染HEK293细胞,应用荧光共振能量转移技术（FRET）进行检测,挑取发生FRET作用的单个细胞,并进行单细胞PcR检测。由此扩增出的片段通过测序和蛋白序列数据库NCBI进行序列比对后,筛选出与CaM产生相互作用的蛋白。目前,已经通过这种方法成功地筛选到了一些与CaM相结合的蛋白,从而为进一步研究CaM蛋白在生理环境下的作用提供有利条件。     

单细胞转录组技术及其在人类细胞图谱构建中的应用

单细胞转录组技术在单细胞水平上进行转录组测序,提供了单个细胞的基因表达差异信息,使在单细胞尺度下研究个体细胞、相关环境细胞及其相互作用的机理成为可能.近年来,单细胞转录组技术在c DNA扩增原理上经历了从末端加尾、体外逆转录到模板置换的方法发展,大大提高了基因检测的数量、基因表达的准确性等.同时,在单细胞选取方式上进行了从96/384孔板到油包水液滴以及纳米微孔的创新,在提高通量和重复性的同时降低了整体实验成本.单细胞转录组技术广泛应用于细胞群体分类和异质性研究,推动了从发育生物学到正常、病态组织细胞图谱的构建.本文对单细胞转录组技术近年的技术进展以及在人类细胞图谱构建中的应用进行了综述.     

Single-cell isolation from cell suspensions and whole genome amplification from single cells to provide templates for CGH analysis

A comprehensive genomic analysis of single cells is instrumental for numerous applications in tumor genetics, clinical diagnostics and forensic analyses. Here, we provide a protocol for single-cell isolation and whole genome amplification, which includes the following stages: preparation of single-cell suspensions from blood or bone marrow samples and cancer cell lines; their characterization on the basis of morphology, interphase fluorescent in situ hybridization pattern and antibody staining; isolation of single cells by either laser microdissection or micromanipulation; and unbiased amplification of single-cell genomes by either linker-adaptor PCR or GenomePlex library technology. This protocol provides a suitable template to screen for chromosomal copy number changes by conventional comparative genomic hybridization (CGH) or array CGH. Expected results include the generation of several micrograms of DNA from single cells, which can be used for CGH or other analyses, such as sequencing. Using linker-adaptor PCR or GenomePlex library technology, the protocol takes 72 or 30 h, respectively.     

Cloning of Plasmodium falciparum by single-cell sorting

Malaria parasite cloning is traditionally carried out mainly by using the limiting dilution method, which is laborious, imprecise, and unable to distinguish multiply-infected RBCs. In this study, we used a parasite engineered to express green fluorescent protein (GFP) to evaluate a single-cell sorting method for rapidly cloning Plasmodium falciparum. By dividing a two-dimensional scattergram from a cell sorter into 17 gates, we determined the parameters for isolating singly-infected erythrocytes and sorted them into individual cultures. Pre-gating of the engineered parasites for GFP allowed the isolation of almost 100% GFP-positive clones. Compared with the limiting dilution method, the number of parasite clones obtained by single-cell sorting was much higher. Molecular analyses showed that parasite isolates obtained by single-cell sorting were highly homogenous. This highly efficient single-cell sorting method should prove very useful for cloning both P. falciparum laboratory populations from genetic manipulation experiments and clinical samples.     

RNA sequencing: new technologies and applications in cancer research

Acute myeloid leukemia (AML) is a fatal hematopoietic malignancy and has a prognosis that varies with its genetic complexity. However, there has been no appropriate integrative analysis on the hierarchy of different AML subtypes. Using Microwell-seq, a high-throughput single-cell mRNA sequencing platform, we analyzed the cellular hierarchy of bone marrow samples from 40 patients and 3 healthy donors. We also used single-cell single-molecule real-time (SMRT) sequencing to investigate the clonal heterogeneity of AML cells. From the integrative analysis of 191727 AML cells, we established a single-cell AML landscape and identified an AML progenitor cell cluster with novel AML markers. Patients with ribosomal protein high progenitor cells had a low remission rate. We deduced two types of AML with diverse clinical outcomes. We traced mitochondrial mutations in the AML landscape by combining Microwell-seq with SMRT sequencing. We propose the existence of a phenotypic “cancer attractor” that might help to define a common phenotype for AML progenitor cells. Finally, we explored the potential drug targets by making comparisons between the AML landscape and the Human Cell Landscape. We identified a key AML progenitor cell cluster. A high ribosomal protein gene level indicates the poor prognosis. We deduced two types of AML and explored the potential drug targets. Our results suggest the existence of a cancer attractor.     

Single cell deposition and patterning with a robotic system

Integrating single-cell manipulation techniques in traditional and emerging biological culture systems is challenging. Microfabricated devices for single cell studies in particular often require cells to be spatially positioned at specific culture sites on the device surface. This paper presents a robotic micromanipulation system for pick-and-place positioning of single cells. By integrating computer vision and motion control algorithms, the system visually tracks a cell in real time and controls multiple positioning devices simultaneously to accurately pick up a single cell, transfer it to a desired substrate, and deposit it at a specified location. A traditional glass micropipette is used, and whole- and partial-cell aspiration techniques are investigated to manipulate single cells. Partially aspirating cells resulted in an operation speed of 15 seconds per cell and a 95% success rate. In contrast, the whole-cell aspiration method required 30 seconds per cell and achieved a success rate of 80%. The broad applicability of this robotic manipulation technique is demonstrated using multiple cell types on traditional substrates and on open-top microfabricated devices, without requiring modifications to device designs. Furthermore, we used this serial deposition process in conjunction with an established parallel cell manipulation technique to improve the efficiency of single cell capture from ～80% to 100%. Using a robotic micromanipulation system to position single cells on a substrate is demonstrated as an effective stand-alone or bolstering technology for single-cell studies, eliminating some of the drawbacks associated with standard single-cell handling and manipulation techniques.