Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.
Chromatin ; Genome ; Single-Cell Analysis/methods* ; Cell Differentiation ; Data Analysis

Single-cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell sub-populations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data (VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.
Computer Graphics ; Gene Expression Profiling ; methods ; Humans ; Sequence Analysis, RNA ; methods ; Single-Cell Analysis

Studies of cellular dynamic processes have shown that cells undergo state changes during dynamic processes, controlled mainly by the expression of genes within the cell. With the development of high-throughput sequencing technologies, the availability of large amounts of gene expression data enables the acquisition of true gene expression information of cells at the single-cell level. However, most existing research methods require the use of information beyond gene expression, thus introducing additional complexity and uncertainty. In addition, the prevalence of dropout events hampers the study of cellular dynamics. To this end, we propose an approach named gene interaction network entropy (GINE) to quantify the state of cell differentiation as a means of studying cellular dynamics. Specifically, by constructing a cell-specific network based on the association between genes through the stability of the network, and defining the GINE, the unstable gene expression data is converted into a relatively stable GINE. This method has no additional complexity or uncertainty, and at the same time circumvents the effects of dropout events to a certain extent, allowing for a more reliable characterization of biological processes such as cell fate. This method was applied to study two single-cell RNA-seq datasets, head and neck squamous cell carcinoma and chronic myeloid leukaemia. The GINE method not only effectively distinguishes malignant cells from benign cells and differentiates between different periods of differentiation, but also effectively reflects the disease efficacy process, demonstrating the potential of using GINE to study cellular dynamics. The method aims to explore the dynamic information at the level of single cell disorganization and thus to study the dynamics of biological system processes. The results of this study may provide scientific recommendations for research on cell differentiation, tracking cancer development, and the process of disease response to drugs.
Cell Differentiation/genetics* ; Entropy ; Gene Regulatory Networks ; High-Throughput Nucleotide Sequencing ; Single-Cell Analysis/methods*

Pathogenic microbes can induce cellular dysfunction, immune response, and cause infectious disease and other diseases including cancers. However, the cellular distributions of pathogens and their impact on host cells remain rarely explored due to the limited methods. Taking advantage of single-cell RNA-sequencing (scRNA-seq) analysis, we can assess the transcriptomic features at the single-cell level. Still, the tools used to interpret pathogens (such as viruses, bacteria, and fungi) at the single-cell level remain to be explored. Here, we introduced PathogenTrack, a python-based computational pipeline that uses unmapped scRNA-seq data to identify intracellular pathogens at the single-cell level. In addition, we established an R package named Yeskit to import, integrate, analyze, and interpret pathogen abundance and transcriptomic features in host cells. Robustness of these tools has been tested on various real and simulated scRNA-seq datasets. PathogenTrack is competitive to the state-of-the-art tools such as Viral-Track, and the first tools for identifying bacteria at the single-cell level. Using the raw data of bronchoalveolar lavage fluid samples (BALF) from COVID-19 patients in the SRA database, we found the SARS-CoV-2 virus exists in multiple cell types including epithelial cells and macrophages. SARS-CoV-2-positive neutrophils showed increased expression of genes related to type I interferon pathway and antigen presenting module. Additionally, we observed the Haemophilus parahaemolyticus in some macrophage and epithelial cells, indicating a co-infection of the bacterium in some severe cases of COVID-19. The PathogenTrack pipeline and the Yeskit package are publicly available at GitHub.
COVID-19 ; Humans ; RNA ; SARS-CoV-2/genetics* ; Single-Cell Analysis/methods* ; Transcriptome

With the development of high throughput sequencing and single-cell genomics technologies, many uncultured bacterial communities have been dissected by combining these two techniques. Especially, by simultaneously leveraging of single-cell genomics and metagenomics, researchers can greatly improve the efficiency and accuracy of obtaining whole genome information from complex microbial communities, which not only allow us to identify microbes but also link function to species, identify subspecies variations, study host-virus interactions and etc. Here, we review recent developments and the challenges need to be addressed in single-cell metagenomics, including potential contamination, uneven sequence coverage, sequence chimera, genome assembly and annotation. With the development of sequencing and computational methods, single-cell metagenomics will undoubtedly broaden its application in various microbiome studies.
Animals ; Bacteria ; genetics ; Computational Biology ; methods ; High-Throughput Nucleotide Sequencing ; methods ; Humans ; Metagenomics ; Single-Cell Analysis ; methods

Clustering is a prevalent analytical means to analyze single cell RNA sequencing (scRNA-seq) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of pressing need. Here we proposed Spearman subsampling-clustering-classification (SSCC), a new clustering framework based on random projection and feature construction, for large-scale scRNA-seq data. SSCC greatly improves clustering accuracy, robustness, and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, SSCC achieved 20% improvement for clustering accuracy and 50-fold acceleration, but only consumed 66% memory usage, compared to the widelyused software package SC3. Compared to k-means, the accuracy improvement of SSCC can reach 3-fold. An R implementation of SSCC is available at https://github.com/Japrin/sscClust.
Algorithms ; Animals ; Cluster Analysis ; Computational Biology ; methods ; Databases as Topic ; Gene Expression Profiling ; methods ; Humans ; Mice ; Sequence Analysis, RNA ; Single-Cell Analysis ; Software ; Statistics, Nonparametric

AIMTo validate the abundance of Interleukin 8 receptor beta (IL-8Rbeta) mRNA in single human neutrophil.

METHODSHuman neutrophils were isolated and purified from volunteers, total RNA was extracted and a regular RT-PCR aiming at IL-8Rbeta mRNA was performed to ascertain its expression profile in human neutrophils and optimize the reaction conditions for the following single-cell RT-PCR procedures. Subsequently, single neutrophil or the cellular content was harvested to conduct reverse transcription and two-round PCR with the same primer pairs used before. Serial dilution of single neutrophil cDNA pool was carried out at the same time with the exact two-round PCR followed. The specificity of this single-cell RT-PCR procedure was verified by the BamHI restriction endonuclease digestion on the final cDNA products.

RESULTSRegular RT-PCR indicated IL-8Rbeta mRNA expression in human neutrophils. While single-cell RT-PCR was sensitive enough to detect trace IL-8Rbeta mRNA as predicted cDNA product could be amplified from a 10 000 times diluted intracellular specimen from single neutrophil, which indicated an abundant expression of this mRNA in human neutrophil. Moreover, BamHI digestion on the final cDNA product clarified the specificity of this single-cell RT-PCR procedure.

CONCLUSIONThis simplified semi-quantitative single-cell RT-PCR procedure specifically confirmed that IL-8Rbeta mRNA was highly expressed in human neutrophil, which also provided the possibility of comparing mRNA abundance at single cell level.

Cells, Cultured ; Humans ; Neutrophils ; chemistry ; RNA, Messenger ; genetics ; Receptors, Interleukin-8B ; analysis ; genetics ; Reverse Transcriptase Polymerase Chain Reaction ; methods ; Single-Cell Analysis ; methods

AIMTo investigate the method of detecting gene expression in colon tissue at a single cell level.

METHODSIndividual cell(s) were picked up from colon frozen section using laser microdissection. RNA was extracted, reverse transcribed to complementary DNA (cDNA). cDNA was then analyzed by nested reverse transcription polymerase chain reaction (nested RT-PCR) using two pairs of primers.

RESULTSSingle cell(s) were selectively picked up using an ultraviolet laser micromanipulator. RNA was extracted, reverse transcribed and used for nested RT-PCR. Amplification products of cDNA from down to a single cell could be clearly visualized in the agarose gel.

CONCLUSIONThe combined utilization of laser microdissection and nested RT-PCR provides an opportunity to analyze gene expression at single cell(s) level in colon tissue.

Colon ; cytology ; Gene Expression ; Gene Expression Profiling ; methods ; Humans ; Laser Capture Microdissection ; Reverse Transcriptase Polymerase Chain Reaction ; methods ; Single-Cell Analysis

Acute Kidney Injury ; etiology ; Betacoronavirus ; Coronavirus Infections ; complications ; Humans ; Kidney ; enzymology ; Pandemics ; Peptidyl-Dipeptidase A ; physiology ; Pneumonia, Viral ; complications ; Sequence Analysis, RNA ; methods ; Serine Endopeptidases ; physiology ; Single-Cell Analysis ; methods

OBJECTIVETo inquire about the relationship of p53 gene mutation to the histopathological findings and clinical manifestation in cases of laryngeal squamous cell carcinoma(LSCC).

METHODSThe fresh samples from 60 cases of LSCC were examined. Polymerase chain reaction and silver staining-single strand conformation polymorphism (PCR-SSCP) and DNA direct sequencing were used to detect the mutation of p53 gene in exons 5-8.

RESULTSThe mutation rates were 69.2% and 85.3% in patients at clinical stage I-II and stage III-IV respectively (P>0.05). In the well-, moderately- and poorly-differentiated cell of LSCC, the mutation rates were 52.9%, 83.3% and 94.7% respectively (P<0.05). The p53 gene mutation rate of LSCC patients with neck lymph-node metastasis was 96.4%, whereas that of patients without neck lymph-node metastasis was 62.5% (P<0.05). Twenty samples showed positive results in SSCP; 19 samples showed deletion and mutation in codons 125-292 by DNA direct sequencing.

CONCLUSIONThe mutation of p53 gene in exons 5-8 was closely related to cell differentiation and the neck lymph-node metastasis of LSCC, but it was not related to the clinical stages of the LSCC cases.

Carcinoma, Squamous Cell ; genetics ; secondary ; Cell Differentiation ; genetics ; Exons ; Humans ; Laryngeal Neoplasms ; genetics ; pathology ; Lymphatic Metastasis ; genetics ; Mutation ; Neoplasm Staging ; Polymerase Chain Reaction ; methods ; Polymorphism, Single-Stranded Conformational ; Sequence Analysis, DNA ; methods ; Tumor Suppressor Protein p53 ; genetics