Single cell transcriptomics view of cell lineage, cell fate and cellular differentiation

A Cell

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

Critical downstream analysis steps for single-cell RNA sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbab105 ◽

2021 ◽

Author(s):

Zilong Zhang ◽

Feifei Cui ◽

Chen Lin ◽

Lingling Zhao ◽

Chunyu Wang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Noisy Data ◽

Single Cell Level ◽

Cell Type ◽

Sequencing Data ◽

Cell Level ◽

Bioinformatics Tool ◽

Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder

10.1101/199315 ◽

2017 ◽

Cited By ~ 6

Author(s):

Dongfang Wang ◽

Jin Gu

Keyword(s):

Dimension Reduction ◽

Single Cell ◽

Rna Sequencing ◽

Original Data ◽

Marker Genes ◽

Single Cell Level ◽

Sequencing Data ◽

Cell Level ◽

Variational Autoencoder ◽

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities in single cell level. It is an important step for studying cell sub-populations and lineages based on scRNA-seq data by finding an effective low-dimensional representation and visualization of the original data. The scRNA-seq data are much noiser than traditional bulk RNA-Seq: in 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. In this study, we proposed VASC (deep Variational Autoencoder for scRNA-seq data), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. It can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on twenty datasets, VASC shows superior performances in most cases and broader dataset compatibility compared with four state-of-the-art dimension reduction methods. Then, for a case study of pre-implantation embryos, VASC successfully re-establishes the cell dynamics and identifies several candidate marker genes associated with the early embryo development.

Power Analysis of Single Cell RNA-Sequencing Experiments

10.1101/073692 ◽

2016 ◽

Cited By ~ 6

Author(s):

Valentine Svensson ◽

Kedar Nath Natarajan ◽

Lam-Ha Ly ◽

Ricardo J Miragaia ◽

Charlotte Labalette ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Developmental Process ◽

Cell Types ◽

Rna Degradation ◽

Published Data ◽

Minute Amount ◽

Trade Offs ◽

AbstractHigh-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published scRNA-seq protocols, which all sequence transcriptomes from a minute amount of starting material. Therefore, a key question is how these methods compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of gene expression. Here, we assessed the sensitivity and accuracy of many published data sets based on standardized spike-ins with a uniform raw data processing pipeline. We developed a flexible and fast UMI counting tool (https://github.com/vals/umis) which is compatible with all UMI based protocols. This allowed us to relate these parameters to sequencing depth, and discuss the trade offs between the different methods. To confirm our results, we performed experiments on cells from the same population using three different protocols. We also investigated the effect of RNA degradation on spike-in molecules, and the average efficiency of scRNA-seq on spike-in molecules versus endogenous RNAs.

Single Cell Gene Transcriptome Analysis of Ovarian Mature Teratomas

Pathology & Oncology Research ◽

10.3389/pore.2021.604228 ◽

2021 ◽

Vol 27 ◽

Author(s):

Sun Shin ◽

Youn Jin Choi ◽

Seung-Hyun Jung ◽

Yeun-Jun Chung ◽

Sug Hyung Lee

Keyword(s):

Single Cell ◽

Germ Cells ◽

Expression Profiles ◽

Cell Types ◽

Cell Tumor ◽

Single Cell Level ◽

Cell Level ◽

Normal Cells ◽

Cell Gene

Teratoma is a type of germ cell tumor that originates from totipotential germ cells that are present in gonads, which can differentiate into any of the cell types found in adult tissues. Ovarian teratomas are usually mature cystic teratomas (OMCTs, also known as dermoid cysts). Chromosome studies in OMCTs show that the chromosomes are uniformly homozygous with karyotype of 46, XX, indicating that they may be parthenogenic tumors that arise from a single ovum after thefirst meiotic division. However, the tissues in OMCTs have been known to be morphologically and immunophenotypically identical to the orthotopic tissues. Currently, expression profiles of tissue components in OMCTs are not known. To identify whether OMCT tissues are expressionally similar to or different from the orthotopic tissues, we adopted single-cell RNA-sequencing (scRNA-seq), and analyzed transcriptomes of individual cells in heterogenous tissues of two OMCTs. We found that transcriptome profiles of the OMCTs at single cell level were not significantly different from those of normal cells in orthotopic locations. The present data suggest that parthenogeneticlly altered OMCTs may not alter expression profiles of inrivirual tissue components in OMCTs.

XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

Science Advances ◽

10.1126/sciadv.abg4755 ◽

2021 ◽

Vol 7 (17) ◽

pp. eabg4755

Author(s):

Youjin Lee ◽

Derek Bogdanoff ◽

Yutong Wang ◽

George C. Hartoularos ◽

Jonathan M. Woo ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Spatial Organization ◽

Cell Types ◽

Mouse Tumor ◽

Spatially Resolved ◽

Distinct Cell ◽

A Cell ◽

Anatomical Space

Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA-seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-associated transcriptomic program in tumor-associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.

Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

Cell & Bioscience ◽

10.1186/s13578-021-00635-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fen Ma ◽

Siwei Zhang ◽

Lianhao Song ◽

Bozhi Wang ◽

Lanlan Wei ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Communication ◽

Cellular Communication ◽

Specific Cell ◽

Single Cell Level ◽

Cell Level ◽

Advantages And Disadvantages ◽

Receptor Interactions ◽

Abstract Background Cellular communication is an essential feature of multicellular organisms. Binding of ligands to their homologous receptors, which activate specific cell signaling pathways, is a basic type of cellular communication and intimately linked to many degeneration processes leading to diseases. Main body This study reviewed the history of ligand-receptor and presents the databases which store ligand-receptor pairs. The recently applications and research tools of ligand-receptor interactions for cell communication at single cell level by using single cell RNA sequencing have been sorted out. Conclusion The summary of the advantages and disadvantages of analysis tools will greatly help researchers analyze cell communication at the single cell level. Learning cell communication based on ligand-receptor interactions by single cell RNA sequencing gives way to developing new target drugs and personalizing treatment.

TMIC-06. MYELOID POPULATIONS AND THE EFFECT OF NEOADJUVANT PD-1 INHIBITION IN THE GLIOBLASTOMA MICROENVIRONMENT: A SURFACEOMIC AND TRANSCRIPTOMIC DISSECTION AT THE SINGLE-CELL LEVEL

Neuro-Oncology ◽

10.1093/neuonc/noz175.1040 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi248-vi248

Author(s):

Aaron Mochizuki ◽

Alexander Lee ◽

Joey Orpilla ◽

Jenny Kienzler ◽

Mildred Galvez ◽

...

Keyword(s):

Brain Tumor ◽

T Cell ◽

Single Cell ◽

Rna Sequencing ◽

Myeloid Cells ◽

Single Cell Level ◽

Mass Cytometry ◽

Cell Level ◽

Abstract INTRODUCTION Glioblastoma (GBM) is the most common malignant brain tumor in adults and is associated with a dismal prognosis. Neoadjuvant anti-PD-1 blockade has demonstrated efficacy in melanoma, non-small cell lung cancer and recurrent GBM; however, responses vary. While T cells have garnered considerable attention in the context of immunotherapy, the role of myeloid cells in the GBM microenvironment remains controversial. METHODS We isolated CD45+ immune populations from patients who underwent brain tumor resection at UCLA. We hypothesized that myeloid cells in glioblastoma contribute to T cell dysfunction; however, this immune suppression can be mitigated by neoadjuvant PD-1 inhibition. To test this, we utilized mass cytometry and single-cell RNA sequencing to characterize these immune populations. RESULTS Mass cytometry profiling of tumor infiltrating lymphocytes from patients with GBM demonstrated a preponderance of CD11b+ myeloid populations (75% versus 25% CD3+). At the transcriptomic level, myeloid cells in newly diagnosed GBMs exhibited decreased expression of CCL4 (loge fold change -1.18, Bonferroni-adjusted P = 1.62x10-254) and its ligands compared to anaplastic astrocytoma. In ranked gene set enrichment analysis, patients who received neoadjuvant pembrolizumab demonstrated enrichment in TNFα-, NFκB- and lipid metabolism-related gene sets by bootstrapped Kolmogorov-Smirnov test (Benjamini-Hochberg adjusted P = 4.74x10-3, 1.45x10-2 and 2.48x10-3, respectively) in tumor-associated myeloid populations. Additionally, single-cell trajectory analysis demonstrated increased CCL4 and decreased ISG15 with neoadjuvant checkpoint inhibition. CONCLUSIONS Here, we utilize mass cytometry and single-cell RNA sequencing to demonstrate the predominance and transcriptomic features of myeloid populations in GBM. Myeloid cells in patients who receive neoadjuvant PD-1 blockade re-express increased levels NFκB, TNFα and CCL4, a cytokine crucial for the recruitment of dendritic cells to the tumor for antigen-specific T cell activation. By delving into the GBM microenvironment at the single-cell level, we hope to better delineate the role of myeloid populations in this uniformly fatal tumor.

The current landscape of single-cell transcriptomics for cancer immunotherapy

Journal of Experimental Medicine ◽

10.1084/jem.20201574 ◽

2020 ◽

Vol 218 (1) ◽

Author(s):

Puneeth Guruprasad ◽

Yong Gu Lee ◽

Ki Hyun Kim ◽

Marco Ruella

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Resistance Mechanisms ◽

Immune Checkpoint Blockade ◽

Adoptive Cell Transfer ◽

Single Cell Level ◽

Tumor Resistance ◽

Cell Level ◽

Genetic Makeup

Immunotherapies such as immune checkpoint blockade and adoptive cell transfer have revolutionized cancer treatment, but further progress is hindered by our limited understanding of tumor resistance mechanisms. Emerging technologies now enable the study of tumors at the single-cell level, providing unprecedented high-resolution insights into the genetic makeup of the tumor microenvironment and immune system that bulk genomics cannot fully capture. Here, we highlight the recent key findings of the use of single-cell RNA sequencing to deconvolute heterogeneous tumors and immune populations during immunotherapy. Single-cell RNA sequencing has identified new crucial factors and cellular subpopulations that either promote tumor progression or leave tumors vulnerable to immunotherapy. We anticipate that the strategic use of single-cell analytics will promote the development of the next generation of successful, rationally designed immunotherapeutics.

Understanding the pathogenesis of infectious diseases by single-cell RNA sequencing

Microbial Cell ◽

10.15698/mic2021.09.759 ◽

2021 ◽

Vol 8 (9) ◽

pp. 208-222

Author(s):

Wanqiu Huang ◽

Danni Wang ◽

Yu-Feng Yao

Keyword(s):

Infectious Diseases ◽

Single Cell ◽

Rna Sequencing ◽

Molecular Mechanisms ◽

Transcriptional Profiling ◽

Single Cell Level ◽

Cell Level ◽

Disease Treatment ◽

Cell Diversity

Infections are highly orchestrated and dynamic processes, which involve both pathogen and host. Transcriptional profiling at the single-cell level enables the analysis of cell diversity, heterogeneity of the immune response, and detailed molecular mechanisms underlying infectious diseases caused by bacteria, viruses, fungi, and parasites. Herein, we highlight recent remarkable advances in single-cell RNA sequencing (scRNA-seq) technologies and their applications in the investigation of host-pathogen interactions, current challenges and potential prospects for disease treatment are discussed as well. We propose that with the aid of scRNA-seq, the mechanism of infectious diseases will be further revealed thus inspiring the development of novel interventions and therapies.