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Single-cell RNA-sequencing (scRNA-seq) techniques provide unprecedented opportunities to investigate phenotypic and molecular heterogeneity in complex biological systems. However, profiling massive amounts of cells brings great computational challenges to accurately and efficiently characterize diverse cell populations. Single cell discriminant analysis (scDA) solves this problem by simultaneously identifying cell groups and discriminant metagenes based on the construction of cell-by-cell representation graph, and then using them to annotate unlabeled cells in data. We demonstrate scDA is effective to determine cell types, revealing the overall variabilities between cells from eleven data sets. scDA also outperforms several state-of-the-art methods when inferring the labels of new samples. In particular, we found scDA less sensitive to drop-out events and capable to label a mass of cells within or across datasets after learning even from a small set of data. The scDA approach offers a new way to efficiently analyze scRNA-seq profiles of large size or from different batches. scDA was implemented and freely available at https://github.com/ZCCQQWork/scDA.
The single-cell capture microfluidic chip has many advantages, including low cost, high throughput, easy manufacturing, integration, non-toxicity and good stability. Because of these characteristics, the cell capture microfluidic chip is increasingly becoming an important carrier on the study of life science and pharmaceutical analysis. Important promises of single-cell analysis are the paring, fusion, disruption and analysis of intracellular components for capturing a single cell. The capture, which is based on the fluid dynamics method in the field of micro fluidic chips is an important way to achieve and realize the operations mentioned above. The aim of this study was to compare the ability of three fluid dynamics-based microfluidic chip structures to capture cells. The effects of cell growth and distribution after being captured by different structural chips and the subsequent observation and analysis of single cells on the chip were compared. It can be seen from the experimental results that the microfluidic chip structure most suitable for single-cell capture is a U-shaped structure. It enables single-cell capture as well as long-term continuous culture and the single-cell observation of captured cells. Compared to the U-shaped structure, the cells captured by the microcavity structure easily overlapped during the culture process and affected the subsequent analysis of single cells. The flow shortcut structure can also be used to capture and observe single cells, however, the shearing force of the fluid caused by the chip structure is likely to cause deformation of the cultured cells. By comparing the cell capture efficiency of the three chips, the reagent loss during the culture process and the cell growth state of the captured cells, we are provided with a theoretical support for the design of a single-cell capture microfluidic chip and a reference for the study of single-cell capture in the future.
Promoter activation drives gene transcriptional output. Here we report generating site-specifically integrated single-copy promoter transgenes and measuring their expression to indicate promoter activities at single-mRNA level. mRNA counts, Pol II density and Pol II firing rates of the Ccnb1 promoter transgene resembled those of the native Ccnb1 gene both among asynchronous cells and during the cell cycle. We observed distinct activation states of the Ccnb1 promoter among G1 and G2/M cells, suggesting cell cycle-independent origin of cell-to-cell variation in Ccnb1 promoter activation. Expressing a dominant-negative mutant of NF-YA, a key transcriptional activator of the Ccnb1 promoter, increased its "OFF"/"ON" time ratios but did not alter Pol II firing rates during the "ON" period. Furthermore, comparing H3K4me2 and H3K79me2 levels at the Ccnb1 promoter transgene and the native Ccnb1 gene indicated that the enrichment of these two active histone marks did not predispose higher transcriptional activities. In summary, this experimental system enables bridging transcription imaging with molecular analysis to provide novel insights into eukaryotic transcriptional regulation.
Single-cell transcriptomic data have rapidly become very popular in genomic science. Genomic science also has a long history of using network models to understand the way in which genes work together to carry out specific biological functions. However, working with single-cell data presents major challenges, such as zero inflation and technical noise. These challenges require methods to be specifically adapted to the context of single-cell data. Recently, much effort has been made to develop the theory behind statistical network models. This has lead to many new models being proposed, and has provided a thorough understanding of the properties of existing models. However, a large amount of this work assumes binary-valued relationships between network nodes, whereas genomic network analysis is traditionally based on continuous-valued correlations between genes. In this paper, we assess several established methods for genomic network analysis, we compare ways that these methods can be adapted to the single-cell context, and we use mixture-models to infer binary-valued relationships based on gene-gene correlations. Based on these binary relationships, we find that excellent results can be achieved by using subnetwork analysis methodology popular amongst network statisticians. This methodology thereby allows detection of functional subnetwork modules within these single-cell genomic networks.
EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.
The complexity of adult neurogenesis is becoming increasingly apparent as we learn more about cellular heterogeneity and diversity of the neurogenic lineages and stem cell niches within the adult brain. This complexity has been unraveled in part due to single-cell and single-nucleus RNA sequencing (sc-RNAseq and sn-RNAseq) studies that have focused on adult neurogenesis. This review summarizes 33 published studies in the field of adult neurogenesis that have used sc- or sn-RNAseq methods to answer questions about the three main regions that host adult neural stem cells (NSCs): the subventricular zone (SVZ), the dentate gyrus (DG) of the hippocampus, and the hypothalamus. The review explores the similarities and differences in methodology between these studies and provides an overview of how these studies have advanced the field and expanded possibilities for the future.
The dynamics of the late stages of the HIV-1 life cycle are poorly documented. Viral replication dynamics are typically measured in populations of infected cells, but asynchrony that is introduced during the early steps of HIV-1 replication complicates the measurement of the progression of subsequent steps and can mask replication dynamics and their variation in individual infected cells. We established microscopy-based methods to dynamically measure HIV-1-encoded reporter gene and antiviral gene expression in individual infected cells. We coupled these measurements with conventional analyses to quantify delays in the HIV-1 replication cycle imposed by the biphasic nature of HIV-1 gene expression and by the assembly-inhibiting property of the matrix domain of Gag. We further related the dynamics of restriction factor (APOBEC3G) removal to the dynamics of HIV-1 replication in individual cells. These studies provide a timeline for key events in the HIV-1 replication cycle, and reveal that the interval between the onset of early and late HIV-1 gene expression is only ~3 h, but matrix causes a ~6-12 h delay in the generation of extracellular virions. Interestingly, matrix delays particle assembly to a time at which APOBEC3G has largely been removed from the cell. Thus, a need to prepare infected cells to be efficient producers of infectious HIV-1 may provide an impetus for programmed delays in HIV-1 virion genesis. Our findings also emphasize the significant heterogeneity in the length of the HIV-1 replication cycle in homogenous cell populations and suggest that a typical infected cell generates new virions for only a few hours at the end of a 48 h lifespan. Therefore, small changes in the lifespan of infected cells might have a large effect on viral yield in a single cycle and the overall clinical course in infected individuals.
Precise spatial positioning and isolation of mammalian cells is a critical component of many single cell experimental methods and biological engineering applications. Although a variety of cell patterning methods have been demonstrated, many of these methods subject cells to high stress environments, discriminate against certain phenotypes, or are a challenge to implement. Here, we demonstrate a rapid, simple, indiscriminate, and minimally perturbing cell patterning method using a laser fabricated polymer stencil. The stencil fabrication process requires no stencil-substrate alignment, and is readily adaptable to various substrate geometries and experiments.
The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
Alternative splicing contributes to the diversity of gene products by producing multiple transcript variants from one gene. Previous studies have revealed highly variable splicing patterns in single cells, but there is still a controversy in the understanding of the simultaneous expression of multiple transcript variants. Here we show that the dominance of a single transcript variant is a common phenomenon in single cells. We analyzed several single-cell RNA sequencing datasets and observed consistent results. Our results demonstrate that single cells tend to express one major transcript variant of a gene, and the diversity of transcript variants in cell populations mainly results from the heterogeneity of splicing pattern in single cells.
While the amount of studies involving single-cell or single-nucleus RNA-sequencing technologies grows exponentially within the biomedical research area, the kidney field requires reference transcriptomic signatures to allocate each cluster its matching cell type. The present meta-analysis of 39 previously published datasets, from 7 independent studies, involving healthy human adult kidney samples, offers a set of 24 distinct consensus kidney cell type signatures. The use of these signatures may help to assure the reliability of cell type identification in future studies involving single-cell and single-nucleus transcriptomics while improving the reproducibility in cell type allocation.
Cell type assignment is a major challenge for all types of high throughput single cell data. In many cases such assignment requires the repeated manual use of external and complementary data sources. To improve the ability to uniformly assign cell types across large consortia, platforms and modalities, we developed Cellar, a software tool that provides interactive support to all the different steps involved in the assignment and dataset comparison process. We discuss the different methods implemented by Cellar, how these can be used with different data types, how to combine complementary data types and how to analyze and visualize spatial data. We demonstrate the advantages of Cellar by using it to annotate several HuBMAP datasets from multi-omics single-cell sequencing and spatial proteomics studies. Cellar is open-source and includes several annotated HuBMAP datasets.
Forensic science has yet to take full advantage of single cell analysis. Its greatest benefit is the ability to alleviate the challenges associated with DNA mixture analysis, which remains a significant hurdle in forensic science. Many of the factors that cause complexity in mixture interpretation are absent in single cell analyses-multiple contributors, varied levels of contribution, and allele masking. This study revisits single cell analyses in the context of forensic identification, introducing previously unseen depth to the characterization of data generated from single cells using a novel pipeline that includes recovery of single cells using the DEPArray NxT and amplification using the PowerPlex Fusion 6c kit with varied PCR cycles (29, 30, and 31). The resulting allelic signal was assessed using analytical thresholds of 10, 100, and 150RFU. The mean peak heights across the sample sets generally increased as cycle number increased, 75.0 ± 85.3, 147.1 ± 172.6, and 226.1 ± 298.2 RFU, for 29, 30, and 31 cycles, respectively. The average proportion of allele/locus dropout was most significantly impacted by changes in the detection threshold, whereas increases in PCR cycle number had less impact. Overall data quality improved notably when increasing PCR from 29 to 30 cycles, less improvement and more volatility was introduced at 31 cycles. The average random match probabilities for the 29, 30, and 31 cycle sets at 150RFU are 1 in 2.4 × 1018 ± 1.46 × 1019, 1 in 1.49 × 1025 ± 5.8 × 1025, and 1 in 1.83 × 1024 ± 8.09 × 1024, respectively. This demonstrates the current power of single cell analysis in removing the need for complex mixture analysis.
Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. We introduce Cell Population Mapping (CPM), a deconvolution algorithm in which reference scRNA-seq profiles are leveraged to infer the composition of cell types and states from bulk transcriptome data ('scBio' CRAN R-package). Analysis of individual variations in lungs of influenza-virus-infected mice reveals that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change is confirmed in subsequent experiments and is further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.
T-cell activation is a key step in the amplification of an immune response. Over the course of an immune response, cells may be chronically stimulated, with some proportion becoming exhausted; an enormous number of molecules are involved in this process. There remain a number of questions about the process, namely: (1) what degree of heterogeneity and plasticity do T-cells exhibit during stimulation? (2) how many unique cell states define chronic stimulation? and (3) what markers discriminate activated from exhausted cells? We addressed these questions by performing single-cell multiomic analysis to simultaneously measure expression of 38 proteins and 399 genes in human T cells expanded in vitro. This approach allowed us to study -with unprecedented depth-how T cells change over the course of chronic stimulation. Comprehensive immunophenotypic and transcriptomic analysis at day 0 enabled a refined characterization of T-cell maturational states and the identification of a donor-specific subset of terminally differentiated T-cells that would have been otherwise overlooked using canonical cell classification schema. As expected, activation downregulated naïve-cell markers and upregulated effector molecules, proliferation regulators, co-inhibitory and co-stimulatory receptors. Our deep kinetic analysis further revealed clusters of proteins and genes identifying unique states of activation, defined by markers temporarily expressed upon 3 days of stimulation (PD-1, CD69, LTA), markers constitutively expressed throughout chronic activation (CD25, GITR, LGALS1), and markers uniquely up-regulated upon 14 days of stimulation (CD39, ENTPD1, TNFDF10); expression of these markers could be associated with the emergence of short-lived cell types. Notably, different ratios of cells expressing activation or exhaustion markers were measured at each time point. These data reveal the high heterogeneity and plasticity of chronically stimulated T cells. Our study demonstrates the power of a single-cell multiomic approach to comprehensively characterize T-cells and to precisely monitor changes in differentiation, activation, and exhaustion signatures during cell stimulation.
Microfluidics has been widely used in single cell analysis. Current protocols allow either spread or round cells to be analyzed. However, the contribution of cell morphology to single cell analysis has not been noted. In this study, four proteins (EGFR, PTEN, pAKT, and pS6) in the EGFR signaling pathway are measured simultaneously using microfluidic image cytometry (MIC) in glioblastoma cells U87. The results show that the MIC technology can reveal different subsets of cells corresponding to the four protein expression levels no matter whether they are round or spread at the time of the measurements. However, sharper distinction is obtained from round cells, which implies that cellular heterogeneity can be better resolved with round cells during in situ protein quantification by imaging cytometry. This study calls attention to the role of cell morphology in single cell analysis. Future studies should examine whether differences in data interpretation resulting from cell morphology could reveal altered biological meanings.
Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.
Background: Persistent viruses such as murine cytomegalovirus (MCMV) and adenovirus-based vaccines induce strong, sustained CD8 + T-cell responses, described as memory "inflation". These retain functionality, home to peripheral organs and are associated with a distinct transcriptional program. Methods: To further define the nature of the transcriptional mechanisms underpinning memory inflation at different sites we used single-cell RNA sequencing of tetramer-sorted cells from MCMV-infected mice, analyzing transcriptional networks in virus-specific populations in the spleen and gut intra-epithelial lymphocytes (IEL). Results: We provide a transcriptional map of T-cell memory and define a module of gene expression, which distinguishes memory inflation in spleen from resident memory T-cells (T RM) in the gut. Conclusions: These data indicate that CD8 + T-cell memory in the gut epithelium induced by persistent viruses and vaccines has a distinct quality from both conventional memory and "inflationary" memory which may be relevant to protection against mucosal infections.
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