Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments

What did we learn from multiple omics studies in asthma?

More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.

SNV identification from single-cell RNA sequencing data

Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions

MOTIVATION

Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce single-cell RNA-seq assembler, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq.

RESULTS

Extensive evaluations on both simulated and biological datasets demonstrated its superiority over the state-of-the-art RNA-seq assemblers including StringTie, Cufflinks and CLASS2. In particular, it showed a remarkable capability of recovering unknown 'novel' isoforms and highly computational efficiency compared to other tools.

AVAILABILITY AND IMPLEMENTATION

scRNAss is free, open-source software available from https://sourceforge.net/projects/single-cell-rna-seq-assembly/files/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Statistical tests for detecting variance effects in quantitative trait studies

Motivation

Identifying variants, both discrete and continuous, that are associated with quantitative traits, or QTs, is the primary focus of quantitative genetics. Most current methods are limited to identifying mean effects, or associations between genotype or covariates and the mean value of a quantitative trait. It is possible, however, that a variant may affect the variance of the quantitative trait in lieu of, or in addition to, affecting the trait mean. Here, we develop a general methodology to identify covariates with variance effects on a quantitative trait using a Bayesian heteroskedastic linear regression model (BTH). We compare BTH with existing methods to detect variance effects across a large range of simulations drawn from scenarios common to the analysis of quantitative traits.

Results

We find that BTH and a double generalized linear model (dglm) outperform classical tests used for detecting variance effects in recent genomic studies. We show BTH and dglm are less likely to generate spurious discoveries through simulations and application to identifying methylation variance QTs and expression variance QTs. We identify four variance effects of sex in the Cardiovascular and Pharmacogenetics study. Our work is the first to offer a comprehensive view of variance identifying methodology. We identify shortcomings in previously used methodology and provide a more conservative and robust alternative. We extend variance effect analysis to a wide array of covariates that enables a new statistical dimension in the study of sex and age specific quantitative trait effects.

Availability and implementation

https://github.com/b2du/bth.

Supplementary information

Supplementary data are available at Bioinformatics online.

Two-phase differential expression analysis for single cell RNA-seq

Motivation

Single-cell RNA-sequencing (scRNA-seq) has brought the study of the transcriptome to higher resolution and makes it possible for scientists to provide answers with more clarity to the question of 'differential expression'. However, most computational methods still stick with the old mentality of viewing differential expression as a simple 'up or down' phenomenon. We advocate that we should fully embrace the features of single cell data, which allows us to observe binary (from Off to On) as well as continuous (the amount of expression) regulations.

Results

We develop a method, termed SC2P, that first identifies the phase of expression a gene is in, by taking into account of both cell- and gene-specific contexts, in a model-based and data-driven fashion. We then identify two forms of transcription regulation: phase transition, and magnitude tuning. We demonstrate that compared with existing methods, SC2P provides substantial improvement in sensitivity without sacrificing the control of false discovery, as well as better robustness. Furthermore, the analysis provides better interpretation of the nature of regulation types in different genes.

Availability and implementation

SC2P is implemented as an open source R package publicly available at https://github.com/haowulab/SC2P.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bayesian inference on stochastic gene transcription from flow cytometry data

Motivation

Transcription in single cells is an inherently stochastic process as mRNA levels vary greatly between cells, even for genetically identical cells under the same experimental and environmental conditions. We present a stochastic two-state switch model for the population of mRNA molecules in single cells where genes stochastically alternate between a more active ON state and a less active OFF state. We prove that the stationary solution of such a model can be written as a mixture of a Poisson and a Poisson-beta probability distribution. This finding facilitates inference for single cell expression data, observed at a single time point, from flow cytometry experiments such as FACS or fluorescence in situ hybridization (FISH) as it allows one to sample directly from the equilibrium distribution of the mRNA population. We hence propose a Bayesian inferential methodology using a pseudo-marginal approach and a recent approximation to integrate over unobserved states associated with measurement error.

Results

We provide a general inferential framework which can be widely used to study transcription in single cells from the kind of data arising in flow cytometry experiments. The approach allows us to separate between the intrinsic stochasticity of the molecular dynamics and the measurement noise. The methodology is tested in simulation studies and results are obtained for experimental multiple single cell expression data from FISH flow cytometry experiments.

Availability and implementation

All analyses were implemented in R. Source code and the experimental data are available at https://github.com/SimoneTiberi/Bayesian-inference-on-stochastic-gene-transcription-from-flow-cytometry-data.

Supplementary information

Supplementary data are available at Bioinformatics online.

Taking Systems Medicine to Heart

Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine.

Role of network-mediated stochasticity in mammalian drug resistance

A major challenge in biology is that genetically identical cells in the same environment can display gene expression stochasticity (noise), which contributes to bet-hedging, drug tolerance, and cell-fate switching. The magnitude and timescales of stochastic fluctuations can depend on the gene regulatory network. Currently, it is unclear how gene expression noise of specific networks impacts the evolution of drug resistance in mammalian cells. Answering this question requires adjusting network noise independently from mean expression. Here, we develop positive and negative feedback-based synthetic gene circuits to decouple noise from the mean for Puromycin resistance gene expression in Chinese Hamster Ovary cells. In low Puromycin concentrations, the high-noise, positive-feedback network delays long-term adaptation, whereas it facilitates adaptation under high Puromycin concentration. Accordingly, the low-noise, negative-feedback circuit can maintain resistance by acquiring mutations while the positive-feedback circuit remains mutation-free and regains drug sensitivity. These findings may have profound implications for chemotherapeutic inefficiency and cancer relapse.

The role of gene expression noise in the evolution of drug resistance in mammalian cells is unclear. Here, by uncoupling noise from mean expression of a drug resistance gene in CHO cells the authors show that noisy expression aids adaptation to high drug levels, but delays it at low drug levels.

scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data

Background

Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data.

Results

We presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools.

Conclusions

scNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5747-5) contains supplementary material, which is available to authorized users.

Truncated Robust Principal Component Analysis and Noise Reduction for Single Cell RNA Sequencing Data

The development of single cell RNA sequencing (scRNA-seq) has enabled innovative approaches to investigating mRNA abundances. In our study, we are interested in extracting the systematic patterns of scRNA-seq data in an unsupervised manner; thus, we have developed two extensions of robust principal component analysis (RPCA). First, we present a truncated version of RPCA (tRPCA), which is much faster and memory efficient. Second, we introduce a noise reduction in tRPCA with L₂ regularization. Unlike RPCA that only considers a low-rank L and sparse S matrices, the proposed method can also extract a noise E matrix inherent in modern genomic data. We demonstrate its usefulness by applying our methods on the peripheral blood mononuclear cell scRNA-seq data. Particularly, the clustering of a low-rank L matrix showcases better classification of unlabeled single cells. Overall, the proposed variants are well suited for high-dimensional and noisy data that are routinely generated in genomics.

Discovery and characterization of variance QTLs in human induced pluripotent stem cells

Quantification of gene expression levels at the single cell level has revealed that gene expression can vary substantially even across a population of homogeneous cells. However, it is currently unclear what genomic features control variation in gene expression levels, and whether common genetic variants may impact gene expression variation. Here, we take a genome-wide approach to identify expression variance quantitative trait loci (vQTLs). To this end, we generated single cell RNA-seq (scRNA-seq) data from induced pluripotent stem cells (iPSCs) derived from 53 Yoruba individuals. We collected data for a median of 95 cells per individual and a total of 5,447 single cells, and identified 235 mean expression QTLs (eQTLs) at 10% FDR, of which 79% replicate in bulk RNA-seq data from the same individuals. We further identified 5 vQTLs at 10% FDR, but demonstrate that these can also be explained as effects on mean expression. Our study suggests that dispersion QTLs (dQTLs) which could alter the variance of expression independently of the mean can have larger fold changes, but explain less phenotypic variance than eQTLs. We estimate 4,015 individuals as a lower bound to achieve 80% power to detect the strongest dQTLs in iPSCs. These results will guide the design of future studies on understanding the genetic control of gene expression variance.

Common genetic variation can alter the level of average gene expression in human tissues, and through changes in gene expression have downstream consequences on cell function, human development, and human disease. However, human tissues are composed of many cells, each with its own level of gene expression. With advances in single cell sequencing technologies, we can now go beyond simply measuring the average level of gene expression in a tissue sample and directly measure cell-to-cell variance in gene expression. We hypothesized that genetic variation could also alter gene expression variance, potentially revealing new insights into human development and disease. To test this hypothesis, we used single cell RNA sequencing to directly measure gene expression variance in multiple individuals, and then associated the gene expression variance with genetic variation in those same individuals. Our results suggest that effects on gene expression variance are smaller than effects on mean expression, relative to how much the phenotypes vary between individuals, and will require much larger studies than previously thought to detect.

A brief review of single-cell transcriptomic technologies

In recent years, there has been an effort to develop new technologies for measuring gene expression and sequence information from thousands of individual cells. Large data sets that were obtained using these 'single cell' technologies have allowed scientists to address fundamental questions in biomedicine ranging from stems cells and development to cancer and immunology. Here, we provide a brief review of recent developments in single-cell technology. Our intention is to provide a quick background for newcomers to the field as well as a deeper description of some of the leading technologies to date.

Single-Cell RNA-Seq of the Pancreatic Islets--a Promise Not yet Fulfilled?

In the past 3 years, we have seen a flurry of publications on single-cell RNA sequencing (RNA-seq) analyses of pancreatic islets from mouse and human. This technology holds the promise to refine cell-type signatures and discover cellular heterogeneity among the canonical endocrine cell types such as the glucagon-producing α and insulin-producing β cells, going as far as suggesting new subtypes. In addition, single-cell RNA-seq has the ability to characterize rare endocrine cell types that are not captured by prior bulk analysis. With transcriptomics data from individual endocrine cells, cellular states can be profiled both along developmental processes and during the emergence of metabolic diseases. However, the promises of this new technology have not yet been met in full. While the methodology for the first time enabled the transcriptional definition of rare endocrine cell types such as ghrelin-producing ɛ cells, some of the conclusions regarding cell-type-specific gene expression changes in type 2 diabetes might need to be revisited once larger sample sizes become available. Data generation and analysis are continuously improving single-cell RNA-seq approaches and are helping us to understand the (mal)adaptations of the islet cells during development, metabolic challenge, and disease.

Data mining for mutation-specific targets in acute myeloid leukemia

Three mutation-specific targeted therapies have recently been approved by the FDA for the treatment of acute myeloid leukemia (AML): midostaurin for FLT3 mutations, enasidenib for relapsed or refractory cases with IDH2 mutations, and ivosidenib for cases with an IDH1 mutation. Together, these agents offer a mutation-directed treatment approach for up to 45% of de novo adult AML cases, a welcome deluge after a prolonged drought. At the same time, a number of computational tools have recently been developed that promise to further accelerate progress in mutation-specific therapy for AML and other cancers. Technical advances together with comprehensively annotated AML tissue banks have resulted in the availability of large and complex data sets for exploration by the end-user, including (i) microarray gene expression, (ii) exome sequencing, (iii) deep sequencing data of sub-clone heterogeneity, (iv) RNA sequencing of gene expression (bulk and single cell), (v) DNA methylation and chromatin, (vi) and germline quantitative trait loci. Yet few clinicians or experimental hematologists have the time or the training to access or analyze these repositories. This review summarizes the data sets and bioinformatic tools currently available to further the discovery of mutation-specific targets with an emphasis on web-based applications that are open, accessible, user-friendly, and do not require coding experience to navigate. We show examples of how available data can be mined to identify potential targets using synthetic lethality, drug repurposing, epigenetic sub-grouping, and proteomic networks while also highlighting strengths and limitations and the need for superior models for validation.

Allele-specific RNA imaging shows that allelic imbalances can arise in tissues through transcriptional bursting

Extensive cell-to-cell variation exists even among putatively identical cells, and there is great interest in understanding how the properties of transcription relate to this heterogeneity. Differential expression from the two gene copies in diploid cells could potentially contribute, yet our ability to measure from which gene copy individual RNAs originated remains limited, particularly in the context of tissues. Here, we demonstrate quantitative, single molecule allele-specific RNA FISH adapted for use on tissue sections, allowing us to determine the chromosome of origin of individual RNA molecules in formaldehyde-fixed tissues. We used this method to visualize the allele-specific expression of Xist and multiple autosomal genes in mouse kidney. By combining these data with mathematical modeling, we evaluated models for allele-specific heterogeneity, in particular demonstrating that apparent expression from only one of the alleles in single cells can arise as a consequence of low-level mRNA abundance and transcriptional bursting.

In mammals, most cells of the body contain two genetic datasets: one from the mother and one from the father, and—in theory—these two sets of information could contribute equally to produce the molecules in a given cell. In practice, however, this is not always the case, which can have dramatic implications for many traits, including visible features (such as fur color) and even disease outcomes. However, it remains difficult to measure the parental origin of individual molecules in a given cell and thus to assess what processes lead to an imbalance of the maternal and paternal contribution. We adapted a microscopy technique—called allele-specific single molecule RNA FISH—that uses a combination of fluorescent tags to specifically label one type of molecule, RNA, depending on its origin, for use in mouse kidney sections. Focusing on RNAs that were previously reported to show imbalance, we performed measurements and combined these with mathematical modeling to quantify imbalance in tissues and explain how these can arise. We found that we could recapitulate the observed imbalances using models that only take into account the random processes that produce RNA, without the need to invoke special regulatory mechanisms to create unequal contributions.

High throughput single cell separation and identification using a self-priming isometric and Equant screw valve-based (SIES) microfluidic chip

The emergence of various single cell separation and identification platforms has greatly promoted the development of single cell research. Among these platforms, microfluidic chip-based strategies occupy a significant position in single cell separation and identification. Here, we proposed a self-priming isometric and Equant screw valve-based microfluidic chip (SIES chip) for high throughput single cell isolation and identification. With several special designs, such as a peripheral water tank to balance negative pressure distribution in a marginal area of the chip, a screw valve to preserve the suction power during the step-by-step sample loading, and multistage branching "T" shape channels to separate cells evenly into the chambers, up to 2000 single cells can be well dispersed and analyzed at the same time using this chip. We applied this chip for the isolation and identification of single A549 cells targeting the activated leukocyte cell adhesion molecule (ALCAM) gene. The results showed that only a small proportion (approximately 5.1%) of A549 cells expressed ALCAM, which can potentially provide a reference for A549 cell reclassification. Besides being inexpensive, user-friendly and portable, our chip can be used in some resource-limited settings and may have a great potential in POC (Point-of-Care) applications.

Post-GWAS in prostate cancer: from genetic association to biological contribution

Genome-wide association studies (GWAS) have been successful in deciphering the genetic component of predisposition to many human complex diseases including prostate cancer. Germline variants identified by GWAS progressively unravelled the substantial knowledge gap concerning prostate cancer heritability. With the beginning of the post-GWAS era, more and more studies reveal that, in addition to their value as risk markers, germline variants can exert active roles in prostate oncogenesis. Consequently, current research efforts focus on exploring the biological mechanisms underlying specific susceptibility loci known as causal variants by applying novel and precise analytical methods to available GWAS data. Results obtained from these post-GWAS analyses have highlighted the potential of exploiting prostate cancer risk-associated germline variants to identify new gene networks and signalling pathways involved in prostate tumorigenesis. In this Review, we describe the molecular basis of several important prostate cancer-causal variants with an emphasis on using post-GWAS analysis to gain insight into cancer aetiology. In addition to discussing the current status of post-GWAS studies, we also summarize the main molecular mechanisms of potential causal variants at prostate cancer risk loci and explore the major challenges in moving from association to functional studies and their implication in clinical translation.

Differential expression profiles of long non‑coding RNAs during the mouse pronuclear stage under normal gravity and simulated microgravity

Pronuclear migration, which is the initial stage of embryonic development and the marker of zygote formation, is a crucial process during mammalian preimplantation embryonic development. Recent studies have revealed that long non-coding RNAs (lncRNAs) serve an important role in early embryonic development. However, the functional regulation of lncRNAs in this process has yet to be elucidated, largely due to the difficulty of assessing gene expression alterations during the very short time in which pronuclear migration occurs. It has previously been reported that migration of the pronucleus of a zygote can be obstructed by simulated microgravity. To investigate pronuclear migration in mice, a rotary cell culture system was employed, which generates simulated microgravity, in order to interfere with murine pronuclear migration. Subsequently, lncRNA sequencing was performed to investigate the mechanism underlying this process. In the present study, a comprehensive analysis of lncRNA profile during the mouse pronuclear stage was conducted, in which 3,307 lncRNAs were identified based on single-cell RNA sequencing data. Furthermore, 52 lncRNAs were identified that were significantly differentially expressed. Subsequently, 10 lncRNAs were selected for validation by reverse transcription-quantitative polymerase chain reaction, in which the same relative expression pattern was observed. The results revealed that 12 lncRNAs (lnc006745, lnc007956, lnc013100, lnc013782, lnc017097, lnc019869, lnc025838, lnc027046, lnc005454, lnc007956, lnc019410 and lnc019607), with tubulin β 4B class IVb or actinin α 4 as target genes, may be associated with the expression of microtubule and microfilament proteins. Binding association was confirmed using a dual-luciferase reporter assay. Finally, Gene Ontology analysis revealed that the target genes of the differentially expressed lncRNAs participated in cellular processes associated with protein transport, binding, catalytic activity, membrane-bounded organelle, protein complex and the cortical cytoskeleton. These findings suggested that these lncRNAs may be associated with migration of the mouse pronucleus.

Targeting Non-coding RNA in Vascular Biology and Disease

Only recently have we begun to appreciate the importance and complexity of the non-coding genome, owing in some part to truly significant advances in genomic technology such as RNA sequencing and genome-wide profiling studies. Previously thought to be non-functional transcriptional “noise,” non-coding RNAs (ncRNAs) are now known to play important roles in many diverse biological pathways, not least in vascular disease. While microRNAs (miRNA) are known to regulate protein-coding gene expression principally through mRNA degradation, long non-coding RNAs (lncRNAs) can activate and repress genes by a variety of mechanisms at both transcriptional and translational levels. These versatile molecules, with complex secondary structures, may interact with chromatin, proteins, and other RNA to form complexes with an array of functional consequences. A body of emerging evidence indicates that both classes of ncRNAs regulate multiple physiological and pathological processes in vascular physiology and disease. While dozens of miRNAs are now implicated and described in relative mechanistic depth, relatively fewer lncRNAs are well described. However, notable examples include ANRIL, SMILR, and SENCR in vascular smooth muscle cells; MALAT1 and GATA-6S in endothelial cells; and mitochondrial lncRNA LIPCAR as a powerful biomarker. Due to such ubiquitous involvement in pathology and well-known biogenesis and functional genetics, novel miRNA-based therapies and delivery methods are now in development, including some early stage clinical trials. Although lncRNAs may hold similar potential, much more needs to be understood about their relatively complex molecular behaviours before realistic translation into novel therapies. Here, we review the current understanding of the mechanism and function of ncRNA, focusing on miRNAs and lncRNAs in vascular disease and atherosclerosis. We discuss existing therapies and current delivery methods, emphasising the importance of miRNAs and lncRNAs as effectors and biomarkers in vascular pathology.

A web server for comparative analysis of single-cell RNA-seq data

Single cell RNA-Seq (scRNA-seq) studies profile thousands of cells in heterogeneous environments. Current methods for characterizing cells perform unsupervised analysis followed by assignment using a small set of known marker genes. Such approaches are limited to a few, well characterized cell types. We developed an automated pipeline to download, process, and annotate publicly available scRNA-seq datasets to enable large scale supervised characterization. We extend supervised neural networks to obtain efficient and accurate representations for scRNA-seq data. We apply our pipeline to analyze data from over 500 different studies with over 300 unique cell types and show that supervised methods outperform unsupervised methods for cell type identification. A case study highlights the usefulness of these methods for comparing cell type distributions in healthy and diseased mice. Finally, we present scQuery, a web server which uses our neural networks and fast matching methods to determine cell types, key genes, and more.

Publicly available single cell RNA-seq datasets represent valuable resources for comparative and meta-analysis. Here, the authors develop scQuery, a web server integrating over 500 different studies with over 300 unique cell types for comparative analysis of existing and new scRNA-seq data.

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations. In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types. In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Standing Surface Acoustic Wave (SSAW)-Based Fluorescence-Activated Cell Sorter

Microfluidic fluorescence-activated cell sorters (μFACS) have attracted considerable interest because of their ability to identify and separate cells in inexpensive and biosafe ways. Here a high-performance μFACS is presented by integrating a standing surface acoustic wave (SSAW)-based, 3D cell-focusing unit, an in-plane fluorescent detection unit, and an SSAW-based cell-deflection unit on a single chip. Without using sheath flow or precise flow rate control, the SSAW-based cell-focusing technique can focus cells into a single file at a designated position. The tight focusing of cells enables an in-plane-integrated optical detection system to accurately distinguish individual cells of interest. In the acoustic-based cell-deflection unit, a focused interdigital transducer design is utilized to deflect cells from the focused stream within a minimized area, resulting in a high-throughput sorting ability. Each unit is experimentally characterized, respectively, and the integrated SSAW-based FACS is used to sort mammalian cells (HeLa) at different throughputs. A sorting purity of greater than 90% is achieved at a throughput of 2500 events s^-1 . The SSAW-based FACS is efficient, fast, biosafe, biocompatible and has a small footprint, making it a competitive alternative to more expensive, bulkier traditional FACS.

Patch-Seq Protocol to Analyze the Electrophysiology, Morphology and Transcriptome of Whole Single Neurons Derived From Human Pluripotent Stem Cells

The human brain is composed of a complex assembly of about 171 billion heterogeneous cellular units (86 billion neurons and 85 billion non-neuronal glia cells). A comprehensive description of brain cells is necessary to understand the nervous system in health and disease. Recently, advances in genomics have permitted the accurate analysis of the full transcriptome of single cells (scRNA-seq). We have built upon such technical progress to combine scRNA-seq with patch-clamping electrophysiological recording and morphological analysis of single human neurons in vitro. This new powerful method, referred to as Patch-seq, enables a thorough, multimodal profiling of neurons and permits us to expose the links between functional properties, morphology, and gene expression. Here, we present a detailed Patch-seq protocol for isolating single neurons from in vitro neuronal cultures. We have validated the Patch-seq whole-transcriptome profiling method with human neurons generated from embryonic and induced pluripotent stem cells (ESCs/iPSCs) derived from healthy subjects, but the procedure may be applied to any kind of cell type in vitro. Patch-seq may be used on neurons in vitro to profile cell types and states in depth to unravel the human molecular basis of neuronal diversity and investigate the cellular mechanisms underlying brain disorders.

SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks

Gene co-expression network analysis is extremely useful in interpreting a complex biological process. The recent droplet-based single-cell technology is able to generate much larger gene expression data routinely with thousands of samples and tens of thousands of genes. To analyze such a large-scale gene-gene network, remarkable progress has been made in rigorous statistical inference of high-dimensional Gaussian graphical model (GGM). These approaches provide a formal confidence interval or a p-value rather than only a single point estimator for conditional dependence of a gene pair and are more desirable for identifying reliable gene networks. To promote their widespread use, we herein introduce an extensive and efficient R package named SILGGM (Statistical Inference of Large-scale Gaussian Graphical Model) that includes four main approaches in statistical inference of high-dimensional GGM. Unlike the existing tools, SILGGM provides statistically efficient inference on both individual gene pair and whole-scale gene pairs. It has a novel and consistent false discovery rate (FDR) procedure in all four methodologies. Based on the user-friendly design, it provides outputs compatible with multiple platforms for interactive network visualization. Furthermore, comparisons in simulation illustrate that SILGGM can accelerate the existing MATLAB implementation to several orders of magnitudes and further improve the speed of the already very efficient R package FastGGM. Testing results from the simulated data confirm the validity of all the approaches in SILGGM even in a very large-scale setting with the number of variables or genes to a ten thousand level. We have also applied our package to a novel single-cell RNA-seq data set with pan T cells. The results show that the approaches in SILGGM significantly outperform the conventional ones in a biological sense. The package is freely available via CRAN at https://cran.r-project.org/package=SILGGM.

Exosomes molecular diagnostics: Direct conversion of exosomes into the cDNA for gene amplification by two-step polymerase chain reaction

Exosomes are cell derived lipid nanoparticle with a size of 30-100 nm in diameter, found in almost all biological fluids. The composition of the exosomes is mainly lipid, proteins, RNA, DNA, and non-coding RNAs. Currently, most available methods and commercial kits for exosomal-RNA (Exo-RNA) isolation have limitations and shortcomings. Small starting volume of exosomes and the use of extraction/filtration columns results usually insufficient yield of exosomal RNA after isolation. The majority of RNA contained in purified exosomes range in size from 15-500 nucleotides. Some RNA isolation kits are well suited for small RNA transcripts isolation but larger mRNA transcripts are hard to detect. For all of the kits, the cost prize per sample analyzed is very high. Our current method provides a novel way for direct conversion of exosomes into cDNA synthesis (Exo-cDNA) and subsequent gene detection by polymerase chain reaction (PCR). This method has several advantages compared to established available kits. No extraction column is utilized in this procedure which means total recovery of exosomal RNA with maximal yield. In addition, this method is fast and uses a minimal amount of lab supplies, thereby reducing the overall working costs. Our findings suggest that direct conversion of exosomes into cDNA and subsequent gene amplification by two step PCR is a most efficient and reproducible technique. This novel method can be applied to and is useful to advance molecular research of exosomes by solving the problem of low molecular yields.

Visualization of Biomedical Data

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.

SAVER: gene expression recovery for single-cell RNA sequencing

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of lowly and moderately expressed genes which hinders downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for UMI-based scRNA-seq data that borrows information across genes and cells to obtain accurate expression estimates for all genes.

Gene retention, fractionation and subgenome differences in polyploid plants

All natural plant species are evolved from ancient polyploids. Polyloidization plays an important role in plant genome evolution, species divergence and crop domestication. We review how the pattern of polyploidy within the plant phylogenetic tree has engendered hypotheses involving mass extinctions, lag-times following polyploidy, and epochs of asexuality. Polyploidization has happened repeatedly in plant evolution and, we conclude, is important for crop domestication. Once duplicated, the effect of purifying selection on any one duplicated gene is relaxed, permitting duplicate gene and regulatory element loss (fractionation). We review the general topic of fractionation, and how some gene categories are retained more than others. Several explanations, including neofunctionalization, subfunctionalization and gene product dosage balance, have been shown to influence gene content over time. For allopolyploids, genetic differences between parental lines immediately manifest as subgenome dominance in the wide-hybrid, and persist and propagate for tens of millions of years. While epigenetic modifications are certainly involved in genome dominance, it has been difficult to determine which came first, the chromatin marks being measured or gene expression. Data support the conclusion that genome dominance and heterosis are antagonistic and mechanically entangled; both happen immediately in the synthetic wide-cross hybrid. Also operating in this hybrid are mechanisms of 'paralogue interference'. We present a foundation model to explain gene expression and vigour in a wide hybrid/new allotetraploid. This Review concludes that some mechanisms operate immediately at the wide-hybrid, and other mechanisms begin their operations later. Direct interaction of new paralogous genes, as measured using high-resolution chromatin conformation capture, should inform future research and single cell transcriptome sequencing should help achieve specificity while studying gene sub- and neo-functionalization.

ST Pipeline: an automated pipeline for spatial mapping of unique transcripts

Motivation

In recent years we have witnessed an increase in novel RNA-seq based techniques for transcriptomics analysis. Spatial transcriptomics is a novel RNA-seq based technique that allows spatial mapping of transcripts in tissue sections. The spatial resolution adds an extra level of complexity, which requires the development of new tools and algorithms for efficient and accurate data processing.

Results

Here we present a pipeline to automatically and efficiently process RNA-seq data obtained from spatial transcriptomics experiments to generate datasets for downstream analysis.

Availability and implementation

The ST Pipeline is open source under a MIT license and it is available at https://github.com/SpatialTranscriptomicsResearch/st_pipeline.

Contact

jose.fernandez.navarro@scilifelab.se.

Supplementary information

Supplementary data are available at Bioinformatics online.

Programmable DNA switches and their applications

DNA switches are ideally suited for numerous nanotechnological applications, and increasing efforts are being directed toward their engineering. In this review, we discuss how to engineer these switches starting from the selection of a specific DNA-based recognition element, to its adaptation and optimisation into a switch, with applications ranging from sensing to drug delivery, smart materials, molecular transporters, logic gates and others. We provide many examples showcasing their high programmability and recent advances towards their real life applications. We conclude with a short perspective on this exciting emerging field.

Single cell RNA sequencing of stem cell-derived retinal ganglion cells

We used single cell sequencing technology to characterize the transcriptomes of 1,174 human embryonic stem cell-derived retinal ganglion cells (RGCs) at the single cell level. The human embryonic stem cell line BRN3B-mCherry (A81-H7), was differentiated to RGCs using a guided differentiation approach. Cells were harvested at day 36 and prepared for single cell RNA sequencing. Our data indicates the presence of three distinct subpopulations of cells, with various degrees of maturity. One cluster of 288 cells showed increased expression of genes involved in axon guidance together with semaphorin interactions, cell-extracellular matrix interactions and ECM proteoglycans, suggestive of a more mature RGC phenotype.

Single-cell gene expression analysis reveals diversity among human spermatogonia

STUDY QUESTION

Is the molecular profile of human spermatogonia homogeneous or heterogeneous when analysed at the single-cell level?

SUMMARY ANSWER

Heterogeneous expression profiles may be a key characteristic of human spermatogonia, supporting the existence of a heterogeneous stem cell population.

WHAT IS KNOWN ALREADY

Despite the fact that many studies have sought to identify specific markers for human spermatogonia, the molecular fingerprint of these cells remains hitherto unknown.

STUDY DESIGN, SIZE, DURATION

Testicular tissues from patients with spermatogonial arrest (arrest, n = 1) and with qualitatively normal spermatogenesis (normal, n = 7) were selected from a pool of 179 consecutively obtained biopsies. Gene expression analyses of cell populations and single-cells (n = 105) were performed. Two OCT4-positive individual cells were selected for global transcriptional capture using shallow RNA-seq. Finally, expression of four candidate markers was assessed by immunohistochemistry.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Histological analysis and blood hormone measurements for LH, FSH and testosterone were performed prior to testicular sample selection. Following enzymatic digestion of testicular tissues, differential plating and subsequent micromanipulation of individual cells was employed to enrich and isolate human spermatogonia, respectively. Endpoint analyses were qPCR analysis of cell populations and individual cells, shallow RNA-seq and immunohistochemical analyses.

MAIN RESULTS AND THE ROLE OF CHANCE

Unexpectedly, single-cell expression data from the arrest patient (20 cells) showed heterogeneous expression profiles. Also, from patients with normal spermatogenesis, heterogeneous expression patterns of undifferentiated (OCT4, UTF1 and MAGE A4) and differentiated marker genes (BOLL and PRM2) were obtained within each spermatogonia cluster (13 clusters with 85 cells). Shallow RNA-seq analysis of individual human spermatogonia was validated, and a spermatogonia-specific heterogeneous protein expression of selected candidate markers (DDX5, TSPY1, EEF1A1 and NGN3) was demonstrated.

LIMITATIONS, REASONS FOR CAUTION

The heterogeneity of human spermatogonia at the RNA and protein levels is a snapshot. To further assess the functional meaning of this heterogeneity and the dynamics of stem cell populations, approaches need to be developed to facilitate the repeated analysis of individual cells.

WIDER IMPLICATIONS OF THE FINDINGS

Our data suggest that heterogeneous expression profiles may be a key characteristic of human spermatogonia, supporting the model of a heterogeneous stem cell population. Future studies will assess the dynamics of spermatogonial populations in fertile and infertile patients.

LARGE SCALE DATA

RNA-seq data is published in the GEO database: GSE91063.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the Max Planck Society and the Deutsche Forschungsgemeinschaft DFG-Research Unit FOR 1041 Germ Cell Potential (grant numbers SCHO 340/7-1, SCHL394/11-2). The authors declare that there is no conflict of interest.

Modeling the therapeutic efficacy of NFκB synthetic decoy oligodeoxynucleotides (ODNs)

BACKGROUND

Transfection of NF κB synthetic decoy Oligodeoxynucleotides (ODNs) has been proposed as a promising therapeutic strategy for a variety of diseases arising from constitutive activation of the eukaryotic transcription factor NF κB. The decoy approach faces some limitations under physiological conditions notably nuclease-induced degradation.

RESULTS

In this work, we show how a systems pharmacology model of NF κB regulatory networks displaying oscillatory temporal dynamics, can be used to predict quantitatively the dependence of therapeutic efficacy of NF κB synthetic decoy ODNs on dose, unbinding kinetic rates and nuclease-induced degradation rates. Both deterministic mass action simulations and stochastic simulations of the systems biology model show that the therapeutic efficacy of synthetic decoy ODNs is inversely correlated with unbinding kinetic rates, nuclease-induced degradation rates and molecular stripping rates, but is positively correlated with dose. We show that the temporal coherence of the stochastic dynamics of NF κB regulatory networks is most sensitive to adding NF κB synthetic decoy ODNs having unbinding time-scales that are in-resonance with the time-scale of the limit cycle of the network.

CONCLUSIONS

The pharmacokinetics/pharmacodynamics (PK/PD) predicted by the systems-level model should provide quantitative guidance for in-depth translational research of optimizing the thermodynamics/kinetic properties of synthetic decoy ODNs.

Single-Cell Transcriptome Analysis Using SINCERA Pipeline

Genome-scale single-cell biology has recently emerged as a powerful technology with important implications for both basic and medical research. There are urgent needs for the development of computational methods or analytic pipelines to facilitate large amounts of single-cell RNA-Seq data analysis. Here, we present a detailed protocol for SINCERA (SINgle CEll RNA-Seq profiling Analysis), a generally applicable analytic pipeline for processing single-cell data from a whole organ or sorted cells. The pipeline supports the analysis for the identification of major cell types, cell type-specific gene signatures, and driving forces of given cell types. In this chapter, we provide step-by-step instructions for the functions and features of SINCERA together with application examples to provide a practical guide for the research community. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html .

Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each droplet containing a single cell (singlet) and detect droplets containing two cells (doublets). These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 single-nucleotide polymorphisms (SNPs) per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of eight pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell-type-specific changes in gene expression in 8 pooled lupus patient samples treated with interferon (IFN)-β and perform eQTL analysis on 23 pooled samples.

Comprehensive single-cell transcriptional profiling of a multicellular organism

To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold "shotgun" cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type-specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.

RNA expression in human retina

Recent Genome-wide Association Studies (GWASs) for eye diseases/traits have delivered a number of novel findings across a diverse range of diseases, including age-related macular degeneration (AMD), glaucoma and refractive error. However, despite this astonishing rate of success, the major challenge still remains to not only confirm that the genes implicated in these studies are truly the genes conferring protection from or risk of disease but also to define the functional roles these genes play in disease. Ongoing evidence is accumulating that the single nucleotide polymorphisms (SNPs) used in GWAS and fine mapping studies have causal effects through their influence on gene expression rather than affecting protein function. The biological interpretation of SNP regulatory effects for a tissue requires knowledge of the transcriptome for that tissue. We summarize the reasons to characterize the complete retinal transcriptome as well as the evidence to include an assessment of differences in regional retinal expression.

MicroRNA-133b Negatively Regulates Zebrafish Single Mauthner-Cell Axon Regeneration through Targeting tppp3 in Vivo

Axon regeneration, fundamental to nerve repair, and functional recovery, relies on rapid changes in gene expression attributable to microRNA (miRNA) regulation. MiR-133b has been proved to play an important role in different organ regeneration in zebrafish, but its role in regulating axon regeneration in vivo is still controversial. Here, combining single-cell electroporation with a vector-based miRNA-expression system, we have modulated the expression of miR-133b in Mauthner-cells (M-cells) at the single-cell level in zebrafish. Through in vivo imaging, we show that overexpression of miR-133b inhibits axon regeneration, whereas down-regulation of miR-133b, promotes axon outgrowth. We further show that miR-133b regulates axon regeneration by directly targeting a novel regeneration-associated gene, tppp3, which belongs to Tubulin polymerization-promoting protein family. Gain or loss-of-function of tppp3 experiments indicated that tppp3 was a novel gene that could promote axon regeneration. In addition, we observed a reduction of mitochondrial motility, which have been identified to have a positive correlation with axon regeneration, in miR-133b overexpressed M-cells. Taken together, our work provides a novel way to study the role of miRNAs in individual cell and establishes a critical cell autonomous role of miR-133b in zebrafish M-cell axon regeneration. We propose that up-regulation of the newly founded regeneration-associated gene tppp3 may enhance axonal regeneration.

Associations between novel genetic variants in the promoter region of MALAT1 and risk of colorectal cancer

The metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a well-known long non-coding RNA, is involved in pathogenesis and progress of multiple tumors. However, no study has been performed to investigate the relationship between the genetic variants in promoter region of MALAT1 and colorectal cancer risk. In this study, we conducted a two-stage case-control study to evaluate whether MALAT1 genetic variants were associated with colorectal cancer risk. We identified that a single nucleotide polymorphism (SNP) rs1194338 was significantly associated with the decreased colorectal cancer risk with an odds ratio (OR) of 0.70 [95% confidence interval (CI) = 0.49-0.99] in the combined stage. The subsequently stratified analyses showed that the protective effect of rs1194338 was more pronounced in several subgroups. Furthermore, gene expression profiling analysis revealed overexpression of MALAT1 mRNA in colorectal cancer tissue compared with normal controls. Confirmation studies with large sample size and further mechanistic investigations into the function of MALAT1 and its genetic variants are warranted to advance our understanding of their roles in colorectal carcinogenesis, and to aid in the development of novel and targeted therapeutic strategies.

Correction for adipocyte size and number in the analysis of differences in gene expression in fat tissues

In gene expression studies of candidate genes related to fat deposition, accounting for differences in cell number using reference genes could be not sufficient when cell transcriptional levels are related to cell size, or the tissues are constituted by different types of cells where candidate genes could be differentially expressed. In these situations, mixed model can be applied giving the possibility to take into account the effects of adipocyte size and number on gene expression. The inclusion in the models of analysis of adipocyte size and number, previously estimated taking into account the possible bimodality of size distribution, reduces the rate of false positives in the expression of candidate genes, although, as expected, more powerful designs are needed to detect true differences. The analysis of cellularity of adipose tissue is recommended to infer differences in the expression of genes related to fat deposition.

A short review of variants calling for single-cell-sequencing data with applications

The field of single-cell sequencing is fleetly expanding, and many techniques have been developed in the past decade. With this technology, biologists can study not only the heterogeneity between two adjacent cells in the same tissue or organ, but also the evolutionary relationships and degenerative processes in a single cell. Calling variants is the main purpose in analyzing single cell sequencing (SCS) data. Currently, some popular methods used for bulk-cell-sequencing data analysis are tailored directly to be applied in dealing with SCS data. However, SCS requires an extra step of genome amplification to accumulate enough quantity for satisfying sequencing needs. The amplification yields large biases and thus raises challenge for using the bulk-cell-sequencing methods. In order to provide guidance for the development of specialized analyzed methods as well as using currently developed tools for SNS, this paper aims to bridge the gap. In this paper, we firstly introduced two popular genome amplification methods and compared their capabilities. Then we introduced a few popular models for calling single-nucleotide polymorphisms and copy-number variations. Finally, break-through applications of SNS were summarized to demonstrate its potential in researching cell evolution.