Cell lineage inference from SNP and scRNA-Seq data

Critical transition and reversion of tumorigenesis

Cancer is caused by the accumulation of genetic alterations and therefore has been historically considered to be irreversible. Intriguingly, several studies have reported that cancer cells can be reversed to be normal cells under certain circumstances. Despite these experimental observations, conceptual and theoretical frameworks that explain these phenomena and enable their exploration in a systematic way are lacking. In this review, we provide an overview of cancer reversion studies and describe recent advancements in systems biological approaches based on attractor landscape analysis. We suggest that the critical transition in tumorigenesis is an important clue for achieving cancer reversion. During tumorigenesis, a critical transition may occur at a tipping point, where cells undergo abrupt changes and reach a new equilibrium state that is determined by complex intracellular regulatory events. We introduce a conceptual framework based on attractor landscapes through which we can investigate the critical transition in tumorigenesis and induce its reversion by combining intracellular molecular perturbation and extracellular signaling controls. Finally, we present a cancer reversion therapy approach that may be a paradigm-changing alternative to current cancer cell-killing therapies.

Recurrent RNA edits in human preimplantation potentially enhance maternal mRNA clearance

Posttranscriptional modification plays an important role in key embryonic processes. Adenosine-to-inosine RNA editing, a common example of such modifications, is widespread in human adult tissues and has various functional impacts and clinical consequences. However, whether it persists in a consistent pattern in most human embryos, and whether it supports embryonic development, are poorly understood. To address this problem, we compiled the largest human embryonic editome from 2,071 transcriptomes and identified thousands of recurrent embryonic edits (>=50% chances of occurring in a given stage) for each early developmental stage. We found that these recurrent edits prefer exons consistently across stages, tend to target genes related to DNA replication, and undergo organized loss in abnormal embryos and embryos from elder mothers. In particular, these recurrent edits are likely to enhance maternal mRNA clearance, a possible mechanism of which could be introducing more microRNA binding sites to the 3'-untranslated regions of clearance targets. This study suggests a potentially important, if not indispensable, role of RNA editing in key human embryonic processes such as maternal mRNA clearance; the identified editome can aid further investigations.

Simultaneous brain cell type and lineage determined by scRNA-seq reveals stereotyped cortical development

Mutations are acquired frequently, such that each cell's genome inscribes its history of cell divisions. Common genomic alterations involve loss of heterozygosity (LOH). LOH accumulates throughout the genome, offering large encoding capacity for inferring cell lineage. Using only single-cell RNA sequencing (scRNA-seq) of mouse brain cells, we found that LOH events spanning multiple genes are revealed as tracts of monoallelically expressed, constitutionally heterozygous single-nucleotide variants (SNVs). We simultaneously inferred cell lineage and marked developmental time points based on X chromosome inactivation and the total number of LOH events while identifying cell types from gene expression patterns. Our results are consistent with progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis. This type of retrospective analysis could be incorporated into scRNA-seq pipelines and, compared with experimental approaches for determining lineage in model organisms, is applicable where genetic engineering is prohibited, such as humans.

LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis

Lineage analysis is an important assay for developmental biology, cancer biology, etc. Traditional tools in this field are time consuming, technically challenging, and in demand of preexisting knowledge. By integrating exogenous barcodes into cells, single-cell RNA-sequencing (scRNA-seq) can be used to conduct such tasks, but these assays required significant expertise in both wet- and dry-laboratory experiments. We developed a user-friendly algorithm to conduct cell-lineage inference solely based on endogenous markers of label-free scRNA-seq. This algorithm is able to identify lineage-informative mutations from a bunch of interfering mitochondrial RNA variants with high accuracy and efficiency. With this algorithm, we removed most of the technical hurdles of lineage analysis on scRNA-seq and will dramatically accelerate its application in biological research.

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for biomedical research by providing a variety of valuable information with the advancement of computational tools. Lineage analysis based on scRNA-seq provides key insights into the fate of individual cells in various systems. However, such analysis is limited by several technical challenges. On top of the considerable computational expertise and resources, these analyses also require specific types of matching data such as exogenous barcode information or bulk assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) data. To overcome these technical challenges, we developed a user-friendly computational algorithm called “LINEAGE” (label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis). Aiming to screen out endogenous markers of lineage located on mitochondrial reads from label-free scRNA-seq data to conduct lineage inference, LINEAGE integrates a marker selection strategy by feature subspace separation and de novo “low cross-entropy subspaces” identification. In this process, the mutation type and subspace–subspace “cross-entropy” of features were both taken into consideration. LINEAGE outperformed three other methods, which were designed for similar tasks as testified with two standard datasets in terms of biological accuracy and computational efficiency. Applied on a label-free scRNA-seq dataset of BRAF-mutated cancer cells, LINEAGE also revealed genes that contribute to BRAF inhibitor resistance. LINEAGE removes most of the technical hurdles of lineage analysis, which will remarkably accelerate the discovery of the important genes or cell-lineage clusters from scRNA-seq data.

Redefining intestinal immunity with single-cell transcriptomics

The intestinal immune system represents the largest collection of immune cells in the body and is continually exposed to antigens from food and the microbiota. Here we discuss the contribution of single-cell transcriptomics in shaping our understanding of this complex system. We consider the impact on resolving early intestine development, engagement with the neighbouring microbiota, diversity of intestinal immune cells, compartmentalisation within the intestines and interactions with non-immune cells. Finally, we offer a perspective on open questions about gut immunity that evolving single-cell technologies are well placed to address.

Heterogeneity of cell composition and origin identified by single-cell transcriptomics in renal cysts of patients with autosomal dominant polycystic kidney disease

Rationale: Renal cysts in patients with autosomal dominant polycystic kidney disease (ADPKD) can originate from any nephron segments, including proximal tubules (PT), the loop of Henle (LOH), distal tubules (DT), and collecting ducts (CD). Previous studies mostly used limited cell markers and failed to identify cells negative for these markers. Therefore, the cell composition and origin of ADPKD cyst are still unclear, and mechanisms of cystogenesis of different origins await further exploration.

Methods: We performed single-cell RNA sequencing for the normal kidney tissue and seven cysts derived from superficial or deep layers of the polycystic kidney from an ADPKD patient.

Results: Twelve cell types were identified and analyzed. We found that a renal cyst could be derived either from CD or both PT and LOH. Gene set variation analysis (GSVA) showed that epithelial mesenchymal transition (EMT), TNFA signaling via the NFKB pathways, and xenobiotic metabolism were significantly activated in PT-derived cyst epithelial cells while robust expression of genes involved in G2M Checkpoint, mTORC1 signaling, E2F Targets, MYC Targets V1, MYC Targets V2 were observed in CD-derived cells.

Conclusion: Our results revealed that a single cyst could originate from CD or both PT and LOH, suggesting heterogeneity of polycystic composition and origin. Furthermore, cyst epithelial cells with different origins have different gene set activation.

Significance and limitations of the use of next-generation sequencing technologies for detecting mutational signatures

Next generation sequencing technologies (NGS) have been critical in characterizing the genomic landscape and untangling the genetic heterogeneity of human cancer. Since its advent, NGS has played a pivotal role in identifying the patterns of somatic mutations imprinted on cancer genomes and in deciphering the signatures of the mutational processes that have generated these patterns. Mutational signatures serve as phenotypic molecular footprints of exposures to environmental factors as well as deficiency and infidelity of DNA replication and repair pathways. Since the first roadmap of mutational signatures in human cancer was generated from whole-genome and whole-exome sequencing data, there has been a growing interest to extract mutational signatures from other NGS technologies such as targeted panel sequencing, RNA sequencing, single-cell sequencing, duplex sequencing, reduced representation sequencing, and long-read sequencing. Many of these technologies have their inherent sequencing biases and produce technical artifacts that can confound the extraction of reliable and interpretable mutational signatures. In this review, we highlight the relevance, limitations, and prospects of using different NGS technologies for examining mutational patterns and for deciphering mutational signatures.

Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments

Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers—GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles–SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.

The Role of Cell Tracing and Fate Mapping Experiments in Cardiac Outflow Tract Development, New Opportunities through Emerging Technologies

Whilst knowledge regarding the pathophysiology of congenital heart disease (CHDs) has advanced greatly in recent years, the underlying developmental processes affecting the cardiac outflow tract (OFT) such as bicuspid aortic valve, tetralogy of Fallot and transposition of the great arteries remain poorly understood. Common among CHDs affecting the OFT, is a large variation in disease phenotypes. Even though the different cell lineages contributing to OFT development have been studied for many decades, it remains challenging to relate cell lineage dynamics to the morphologic variation observed in OFT pathologies. We postulate that the variation observed in cellular contribution in these congenital heart diseases might be related to underlying cell lineage dynamics of which little is known. We believe this gap in knowledge is mainly the result of technical limitations in experimental methods used for cell lineage analysis. The aim of this review is to provide an overview of historical fate mapping and cell tracing techniques used to study OFT development and introduce emerging technologies which provide new opportunities that will aid our understanding of the cellular dynamics underlying OFT pathology.

Single-cell RNA sequencing in vision research: Insights into human retinal health and disease

Gene expression provides valuable insight into cell function. As such, vision researchers have frequently employed gene expression studies to better understand retinal physiology and disease. With the advent of single-cell RNA sequencing, expression experiments provide an unparalleled resolution of information. Instead of studying aggregated gene expression across all cells in a heterogenous tissue, single-cell technology maps RNA to an individual cell, which facilitates grouping of retinal and choroidal cell types for further study. Single-cell RNA sequencing has been quickly adopted by both basic and translational vision researchers, and single-cell level gene expression has been studied in the visual systems of animal models, retinal organoids, and primary human retina, RPE, and choroid. These experiments have generated detailed atlases of gene expression and identified new retinal cell types. Likewise, single-cell RNA sequencing investigations have characterized how gene expression changes in the setting of many retinal diseases, including how choroidal endothelial cells are altered in age-related macular degeneration. In addition, this technology has allowed vision researchers to discover drivers of retinal development and model rare retinal diseases with induced pluripotent stem cells. In this review, we will overview the growing number of single-cell RNA sequencing studies in the field of vision research. We will summarize experimental considerations for designing single-cell RNA sequencing experiments and highlight important advancements in retinal, RPE, choroidal, and retinal organoid biology driven by this technology. Finally, we generalize these findings to genes involved in retinal degeneration and outline the future of single-cell expression experiments in studying retinal disease.

Exploring Additional Valuable Information From Single-Cell RNA-Seq Data

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

Single cell genomics and developmental biology: moving beyond the generation of cell type catalogues

Major developmental processes such as gastrulation and early embryogenesis rely on a complex network of cell-cell interactions, chromatin remodeling, and transcriptional regulators. This makes it challenging to study early development when using bulk populations of cells. Recent advances in single-cell technologies have allowed researchers to better understand the interactions between different molecular modalities and the heterogeneities within classically defined cell types. As new single-cell technologies mature, they have the potential of providing a step-change in our understanding of embryogenesis. In this review, we summarize recent advances in single-cell technologies with particular focus on those that lend insight into early organogenesis. We then discuss current pitfalls and implications for future research.

Visualization of cardiovascular development, physiology and disease at the single-cell level: Opportunities and future challenges

Single-cell RNA sequencing (scRNA-seq), a method of transcriptome sequencing at the single-cell level, has recently emerged as a revolutionary technology in the field of biomedical research. Compared to conventional gene expression profiling in bulk, scRNA-seq resolves biological differences among individual cells and enables the identification of rare cell populations that are easily overlooked. This review introduces the method of scRNA-seq, summarizes its applications in the field of cardiovascular disease research, and discusses existing limitations and prospects for future applications.

Cellular diversity and lineage trajectory: insights from mouse single cell transcriptomes

Single cell RNA-sequencing (scRNA-seq) technology has matured to the point that it is possible to generate large single cell atlases of developing mouse embryos. These atlases allow the dissection of developmental cell lineages and molecular changes during embryogenesis. When coupled with single cell technologies for profiling the chromatin landscape, epigenome, proteome and metabolome, and spatial tissue organisation, these scRNA-seq approaches can now collect a large volume of multi-omic data about mouse embryogenesis. In addition, advances in computational techniques have enabled the inference of developmental lineages of differentiating cells, even without explicitly introduced genetic markers. This Spotlight discusses recent advent of single cell experimental and computational methods, and key insights from applying these methods to the study of mouse embryonic development. We highlight challenges in analysing and interpreting these data to complement and expand our knowledge from traditional developmental biology studies in relation to cell identity, diversity and lineage differentiation.

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

BACKGROUND

Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed.

RESULTS

Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies.

CONCLUSIONS

We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing

Virtually all tumors are genetically heterogeneous, containing mutationally-defined subclonal cell populations that often have distinct phenotypes. Single-cell RNA-sequencing has revealed that a variety of tumors are also transcriptionally heterogeneous, but the relationship between expression heterogeneity and subclonal architecture is unclear. Here, we address this question in the context of Acute Myeloid Leukemia (AML) by integrating whole genome sequencing with single-cell RNA-sequencing (using the 10x Genomics Chromium Single Cell 5’ Gene Expression workflow). Applying this approach to five cryopreserved AML samples, we identify hundreds to thousands of cells containing tumor-specific mutations in each case, and use the results to distinguish AML cells (including normal-karyotype AML cells) from normal cells, identify expression signatures associated with subclonal mutations, and find cell surface markers that could be used to purify subclones for further study. This integrative approach for connecting genotype to phenotype is broadly applicable to any sample that is phenotypically and genetically heterogeneous.

The advent of single-cell RNA sequencing has revealed significant transcriptional heterogeneity in cancer, but its relationship to genomic heterogeneity remains unclear. Focusing on acute myeloid leukemia samples, the authors describe a general approach for linking mutation-containing cells to their transcriptional phenotypes using single-cell RNA sequencing data.