Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Perspectives on Bulk-Tissue RNA Sequencing and Single-Cell RNA Sequencing for Cardiac Transcriptomics

The heart has been the center of numerous transcriptomic studies in the past decade. Even though our knowledge of the key organ in our cardiovascular system has significantly increased over the last years, it is still not fully understood yet. In recent years, extensive efforts were made to understand the genetic and transcriptomic contribution to cardiac function and failure in more detail. The advent of Next Generation Sequencing (NGS) technologies has brought many discoveries but it is unable to comprehend the finely orchestrated interactions between and within the various cell types of the heart. With the emergence of single-cell sequencing more than 10 years ago, researchers gained a valuable new tool to enable the exploration of new subpopulations of cells, cell-cell interactions, and integration of multi-omic approaches at a single-cell resolution. Despite this innovation, it is essential to make an informed choice regarding the appropriate technique for transcriptomic studies, especially when working with myocardial tissue. Here, we provide a primer for researchers interested in transcriptomics using NGS technologies.

New horizons in the stormy sea of multimodal single-cell data integration

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.

Single-Cell RNA Sequencing in Yeast Using the 10× Genomics Chromium Device

Single-cell RNA sequencing (scRNA-seq) is emerging as an essential technique for studying the physiology of individual cells in populations. Although well-established and optimized for mammalian cells, research of microorganisms has been faced with major technical challenges for using scRNA-seq, because of their rigid cell wall, smaller cell size and overall lower total RNA content per cell. Here, we describe an easy-to-implement adaptation of the protocol for the yeast Saccharomyces cerevisiae using the 10× Genomics platform, originally optimized for mammalian cells. Introducing Zymolyase, a cell wall-digesting enzyme, to one of the initial steps of single-cell droplet formation allows efficient in-droplet lysis of yeast cells, without affecting the droplet emulsion and further sample processing. In addition, we also describe the downstream data analysis, which combines established scRNA-seq analysis protocols with specific adaptations for yeast, and R-scripts for further secondary analysis of the data.

mDrop-Seq: Massively Parallel Single-Cell RNA-Seq of Saccharomyces cerevisiae and Candida albicans

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies.

A comprehensive transcription factor and DNA-binding motif resource for the construction of gene regulatory networks in Botrytis cinerea and Trichoderma atroviride

Botrytis cinerea and Trichoderma atroviride are two relevant fungi in agricultural systems. To gain insights into these organisms' transcriptional gene regulatory networks (GRNs), we generated a manually curated transcription factor (TF) dataset for each of them, followed by a GRN inference utilizing available sequence motifs describing DNA-binding specificity and global gene expression data. As a proof of concept of the usefulness of this resource to pinpoint key transcriptional regulators, we employed publicly available transcriptomics data and a newly generated dual RNA-seq dataset to build context-specific Botrytis and Trichoderma GRNs under two different biological paradigms: exposure to continuous light and Botrytis-Trichoderma confrontation assays. Network analysis of fungal responses to constant light revealed striking differences in the transcriptional landscape of both fungi. On the other hand, we found that the confrontation of both microorganisms elicited a distinct set of differentially expressed genes with changes in T. atroviride exceeding those in B. cinerea. Using our regulatory network data, we were able to determine, in both fungi, central TFs involved in this interaction response, including TFs controlling a large set of extracellular peptidases in the biocontrol agent T. atroviride. In summary, our work provides a comprehensive catalog of transcription factors and regulatory interactions for both organisms. This catalog can now serve as a basis for generating novel hypotheses on transcriptional regulatory circuits in different experimental contexts.

Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in "transport", which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

Glutamine involvement in nitrogen regulation of cellulase production in fungi

BACKGROUND

Cellulase synthesized by fungi can environment-friendly and sustainably degrades cellulose to fermentable sugars for producing cellulosic biofuels, biobased medicine and fine chemicals. Great efforts have been made to study the regulation mechanism of cellulase biosynthesis in fungi with the focus on the carbon sources, while little attention has been paid to the impact and regulation mechanism of nitrogen sources on cellulase production.

RESULTS

Glutamine displayed the strongest inhibition effect on cellulase biosynthesis in Trichoderma reesei, followed by yeast extract, urea, tryptone, ammonium sulfate and L-glutamate. Cellulase production, cell growth and sporulation in T. reesei RUT-C30 grown on cellulose were all inhibited with the addition of glutamine (a preferred nitrogen source) with no change for mycelium morphology. This inhibition effect was attributed to both L-glutamine itself and the nitrogen excess induced by its presence. In agreement with the reduced cellulase production, the mRNA levels of 44 genes related to the cellulase production were decreased severely in the presence of glutamine. The transcriptional levels of genes involved in other nitrogen transport, ribosomal biogenesis and glutamine biosynthesis were decreased notably by glutamine, while the expression of genes relevant to glutamate biosynthesis, amino acid catabolism, and glutamine catabolism were increased noticeably. Moreover, the transcriptional level of cellulose signaling related proteins ooc1 and ooc2, and the cellular receptor of rapamycin trFKBP12 was increased remarkably, whose deletion exacerbated the cellulase depression influence of glutamine.

CONCLUSION

Glutamine may well be the metabolite effector in nitrogen repression of cellulase synthesis, like the role of glucose plays in carbon catabolite repression. Glutamine under excess nitrogen condition repressed cellulase biosynthesis significantly as well as cell growth and sporulation in T. reesei RUT-C30. More importantly, the presence of glutamine notably impacted the transport and metabolism of nitrogen. Genes ooc1, ooc2, and trFKBP12 are associated with the cellulase repression impact of glutamine. These findings advance our understanding of nitrogen regulation of cellulase production in filamentous fungi, which would aid in the rational design of strains and fermentation strategies for cellulase production in industry.

A molecular toolkit for superorganisms

Social insects, such as ants, bees, wasps, and termites, draw biologists' attention due to their distinctive lifestyles. As experimental systems, they provide unique opportunities to study organismal differentiation, division of labor, longevity, and the evolution of development. Ants are particularly attractive because several ant species can be propagated in the laboratory. However, the same lifestyle that makes social insects interesting also hampers the use of molecular genetic techniques. Here, we summarize the efforts of the ant research community to surmount these hurdles and obtain novel mechanistic insight into the biology of social insects. We review current approaches and propose novel ones involving genomics, transcriptomics, chromatin and DNA methylation profiling, RNA interference (RNAi), and genome editing in ants and discuss future experimental strategies.

Mutational sources of trans-regulatory variation affecting gene expression in Saccharomyces cerevisiae

Heritable variation in a gene’s expression arises from mutations impacting cis- and trans-acting components of its regulatory network. Here, we investigate how trans-regulatory mutations are distributed within the genome and within a gene regulatory network by identifying and characterizing 69 mutations with trans-regulatory effects on expression of the same focal gene in Saccharomyces cerevisiae. Relative to 1766 mutations without effects on expression of this focal gene, we found that these trans-regulatory mutations were enriched in coding sequences of transcription factors previously predicted to regulate expression of the focal gene. However, over 90% of the trans-regulatory mutations identified mapped to other types of genes involved in diverse biological processes including chromatin state, metabolism, and signal transduction. These data show how genetic changes in diverse types of genes can impact a gene’s expression in trans, revealing properties of trans-regulatory mutations that provide the raw material for trans-regulatory variation segregating within natural populations.

Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data

Cell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost.

Enhancing Our Understanding of Plant Cell-to-Cell Interactions Using Single-Cell Omics

Plants are composed of cells that physically interact and constantly adapt to their environment. To reveal the contribution of each plant cells to the biology of the entire organism, their molecular, morphological, and physiological attributes must be quantified and analyzed in the context of the morphology of the plant organs. The emergence of single-cell/nucleus omics technologies now allows plant biologists to access different modalities of individual cells including their epigenome and transcriptome to reveal the unique molecular properties of each cell composing the plant and their dynamic regulation during cell differentiation and in response to their environment. In this manuscript, we provide a perspective regarding the challenges and strategies to collect plant single-cell biological datasets and their analysis in the context of cellular interactions. As an example, we provide an analysis of the transcriptional regulation of the Arabidopsis genes controlling the differentiation of the root hair cells at the single-cell level. We also discuss the perspective of the use of spatial profiling to complement existing plant single-cell omics.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

Differential Expression Levels of Sox9 in Early Neocortical Radial Glial Cells Regulate the Decision between Stem Cell Maintenance and Differentiation

Radial glial progenitor cells (RGCs) in the dorsal telencephalon directly or indirectly produce excitatory projection neurons and macroglia of the neocortex. Recent evidence shows that the pool of RGCs is more heterogeneous than originally thought and that progenitor subpopulations can generate particular neuronal cell types. Using single-cell RNA sequencing, we have studied gene expression patterns of RGCs with different neurogenic behavior at early stages of cortical development. At this early age, some RGCs rapidly produce postmitotic neurons, whereas others self-renew and undergo neurogenic divisions at a later age. We have identified candidate genes that are differentially expressed among these early RGC subpopulations, including the transcription factor Sox9. Using in utero electroporation in embryonic mice of either sex, we demonstrate that elevated Sox9 expression in progenitors affects RGC cell cycle duration and leads to the generation of upper layer cortical neurons. Our data thus reveal molecular differences between progenitor cells with different neurogenic behavior at early stages of corticogenesis and indicates that Sox9 is critical for the maintenance of RGCs to regulate the generation of upper layer neurons.

SIGNIFICANCE STATEMENT The existence of heterogeneity in the pool of RGCs and its relationship with the generation of cellular diversity in the cerebral cortex has been an interesting topic of debate for many years. Here we describe the existence of RGCs with reduced neurogenic behavior at early embryonic ages presenting a particular molecular signature. This molecular signature consists of differential expression of some genes including the transcription factor Sox9, which has been found to be a specific regulator of this subpopulation of progenitor cells. Functional experiments perturbing expression levels of Sox9 reveal its instructive role in the regulation of the neurogenic behavior of RGCs and its relationship with the generation of upper layer projection neurons at later ages.

A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful experimental approach to study cellular heterogeneity. One of the challenges in scRNA-seq data analysis is integrating different types of biological data to consistently recognize discrete biological functions and regulatory mechanisms of cells, such as transcription factor activities and gene regulatory networks in distinct cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. The Bayesian inference transcription factor activity model (BITFAM) integrates ChIP-seq transcription factor binding information into scRNA-seq data analysis. We show that the inferred transcription factor activities for key cell types identify regulatory transcription factors that are known to mechanistically control cell function and cell fate. The BITFAM approach not only identifies biologically meaningful transcription factor activities, but also provides valuable insights into underlying transcription factor regulatory mechanisms.

Single cell gene regulatory networks in plants: Opportunities for enhancing climate change stress resilience

Global warming poses major challenges for plant survival and agricultural productivity. Thus, efforts to enhance stress resilience in plants are key strategies for protecting food security. Gene regulatory networks (GRNs) are a critical mechanism conferring stress resilience. Until recently, predicting GRNs of the individual cells that make up plants and other multicellular organisms was impeded by aggregate population scale measurements of transcriptome and other genome-scale features. With the advancement of high-throughput single cell RNA-seq and other single cell assays, learning GRNs for individual cells is now possible, in principle. In this article, we report on recent advances in experimental and analytical methodologies for single cell sequencing assays especially as they have been applied to the study of plants. We highlight recent advances and ongoing challenges for scGRN prediction, and finally, we highlight the opportunity to use scGRN discovery for studying and ultimately enhancing abiotic stress resilience in plants.

Bench-to-Bedside in Vascular Medicine: Optimizing the Translational Pipeline for Patients With Peripheral Artery Disease

Peripheral arterial disease is a growing worldwide problem with a wide spectrum of clinical severity and is projected to consume >$21 billion per year in the United States alone. While vascular researchers have brought several therapies to the clinic in recent years, few of these approaches have leveraged advances in high-throughput discovery screens, novel translational models, or innovative trial designs. In the following review, we discuss recent advances in unbiased genomics and broader omics technology platforms, along with preclinical vascular models designed to enhance our understanding of disease pathobiology and prioritize targets for additional investigation. Furthermore, we summarize novel approaches to clinical studies in subjects with claudication and ischemic ulceration, with an emphasis on streamlining and accelerating bench-to-bedside translation. By providing a framework designed to enhance each aspect of future clinical development programs, we hope to enrich the pipeline of therapies that may prevent loss of life and limb for those with peripheral arterial disease.

Fusion of single-cell transcriptome and DNA-binding data, for genomic network inference in cortical development

BACKGROUND

Network models are well-established as very useful computational-statistical tools in cell biology. However, a genomic network model based only on gene expression data can, by definition, only infer gene co-expression networks. Hence, in order to infer gene regulatory patterns, it is necessary to also include data related to binding of regulatory factors to DNA.

RESULTS

We propose a new dynamic genomic network model, for inferring patterns of genomic regulatory influence in dynamic processes such as development. Our model fuses experiment-specific gene expression data with publicly available DNA-binding data. The method we propose is computationally efficient, and can be applied to genome-wide data with tens of thousands of transcripts. Thus, our method is well suited for use as an exploratory tool for genome-wide data. We apply our method to data from human fetal cortical development, and our findings confirm genomic regulatory patterns which are recognised as being fundamental to neuronal development.

CONCLUSIONS

Our method provides a mathematical/computational toolbox which, when coupled with targeted experiments, will reveal and confirm important new functional genomic regulatory processes in mammalian development.

A genome-scale yeast library with inducible expression of individual genes

The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3 EB42, that gives lower expression than Z3 EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

Single-cell RNA sequencing of batch Chlamydomonas cultures reveals heterogeneity in their diurnal cycle phase

The photosynthetic unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) is a versatile reference for algal biology because of its ease of culture in the laboratory. Genomic and systems biology approaches have previously described transcriptome responses to environmental changes using bulk data, thus representing the average behavior from pools of cells. Here, we apply single-cell RNA sequencing (scRNA-seq) to probe the heterogeneity of Chlamydomonas cell populations under three environments and in two genotypes differing by the presence of a cell wall. First, we determined that RNA can be extracted from single algal cells with or without a cell wall, offering the possibility to sample natural algal communities. Second, scRNA-seq successfully separated single cells into nonoverlapping cell clusters according to their growth conditions. Cells exposed to iron or nitrogen deficiency were easily distinguished despite a shared tendency to arrest photosynthesis and cell division to economize resources. Notably, these groups of cells not only recapitulated known patterns observed with bulk RNA-seq but also revealed their inherent heterogeneity. A substantial source of variation between cells originated from their endogenous diurnal phase, although cultures were grown in constant light. We exploited this result to show that circadian iron responses may be conserved from algae to land plants. We document experimentally that bulk RNA-seq data represent an average of typically hidden heterogeneity in the population.

Bayesian Network Analysis of Lysine Biosynthesis Pathway in Rice

Lysine is the first limiting essential amino acid in rice because it is present in the lowest quantity compared to all the other amino acids. Amino acids are the building block of proteins and play an essential role in maintaining the human body’s healthy functioning. Rice is a staple food for more than half of the global population; thus, increasing the lysine content in rice will help improve global health. In this paper, we studied the lysine biosynthesis pathway in rice (Oryza sativa) to identify the regulators of the lysine reporter gene LYSA (LOC_Os02g24354). Genetically intervening at the regulators has the potential to increase the overall lysine content in rice. We modeled the lysine biosynthesis pathway in rice seedlings under normal and saline (NaCl) stress conditions using Bayesian networks. We estimated the model parameters using experimental data and identified the gene DAPF(LOC_Os12g37960) as a positive regulator of the lysine reporter gene LYSA under both normal and saline stress conditions. Based on this analysis, we conclude that the gene DAPF is a potent candidate for genetic intervention. Upregulating DAPF using methods such as CRISPR-Cas9 gene editing strategy has the potential to upregulate the lysine reporter gene LYSA and increase the overall lysine content in rice.

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

Revealing lineage-related signals in single-cell gene expression using random matrix theory

Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contains rich information about biological state, including cell type, cell cycle phase, gene regulatory patterns, and location within the tissue of origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. Here, we use a recent advance in random matrix theory, discovered in the context of protein phylogeny, to identify differentiation or ancestry-related processes in single-cell data. Qin and Colwell [C. Qin, L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A. 115, 690-695 (2018)] showed that ancestral relationships in protein sequences create a power-law signature in the covariance eigenvalue distribution. We demonstrate the existence of such signatures in scRNA-seq data and that the genes driving them are indeed related to differentiation and developmental pathways. We predict the existence of similar power-law signatures for cells along linear trajectories and demonstrate this for linearly differentiating systems. Furthermore, we generalize to show that the same signatures can arise for cells along tissue-specific spatial trajectories. We illustrate these principles in diverse tissues and organisms, including the mammalian epidermis and lung, Drosophila whole-embryo, adult Hydra, dendritic cells, the intestinal epithelium, and cells undergoing induced pluripotent stem cells (iPSC) reprogramming. We show how these results can be used to interpret the gradual dynamics of lineage structure along iPSC reprogramming. Together, we provide a framework that can be used to identify signatures of specific biological processes in single-cell data without prior knowledge and identify candidate genes associated with these processes.

Single-Cell RNA Sequencing of the Adult Mammalian Heart-State-of-the-Art and Future Perspectives

Purpose of the Review

Cardiovascular disease remains the leading cause of death worldwide, resulting in cardiac dysfunction and, subsequently, heart failure (HF). Single-cell RNA sequencing (scRNA-seq) is a rapidly developing tool for studying the transcriptional heterogeneity in both healthy and diseased hearts. Wide applications of techniques like scRNA-seq could significantly contribute to uncovering the molecular mechanisms involved in the onset and progression to HF and contribute to the development of new, improved therapies. This review discusses several studies that successfully applied scRNA-seq to the mouse and human heart using various methods of tissue processing and downstream analysis.

Recent Findings

The application of scRNA-seq in the cardiovascular field is continuously expanding, providing new detailed insights into cardiac pathophysiology.

Summary

Increased understanding of cardiac pathophysiology on the single-cell level will contribute to the development of novel, more effective therapeutic strategies. Here, we summarise the possible application of scRNA-seq to the adult mammalian heart.

Discriminating the single-cell gene regulatory networks of human pancreatic islets: A novel deep learning application

Analysis of single-cell pancreatic data can play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, inference of single-cell gene regulatory networks remains a challenge. Since recent studies have reported the reliable inference of single-cell gene regulatory networks (SCGRNs), the current study focused on discriminating the SCGRNs of T2D patients from those of healthy controls. By accurately distinguishing SCGRNs of healthy pancreas from those of T2D pancreas, it would be possible to annotate, organize, visualize, and identify common patterns of SCGRNs in metabolic diseases. Such annotated SCGRNs could play an important role in accelerating the process of building large data repositories. This study aimed to contribute to the development of a novel deep learning (DL) application. First, we generated a dataset consisting of 224 SCGRNs belonging to both T2D and healthy pancreas and made it freely available. Next, we chose seven DL architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, DenseNet121, and DenseNet169, trained each of them on the dataset, and checked their prediction based on a test set. Of note, we evaluated the DL architectures on a single NVIDIA GeForce RTX 2080Ti GPU. Experimental results on the whole dataset, using several performance measures, demonstrated the superiority of VGG19 DL model in the automatic classification of SCGRNs, derived from the single-cell pancreatic data.

Cellular quiescence in budding yeast

Cellular quiescence, the temporary and reversible exit from proliferative growth, is the predominant state of all cells. However, our understanding of the biological processes and molecular mechanisms that underlie cell quiescence remains incomplete. As with the mitotic cell cycle, budding and fission yeast are preeminent model systems for studying cellular quiescence owing to their rich experimental toolboxes and the evolutionary conservation across eukaryotes of pathways and processes that control quiescence. Here, we review current knowledge of cell quiescence in budding yeast and how it pertains to cellular quiescence in other organisms, including multicellular animals. Quiescence entails large-scale remodeling of virtually every cellular process, organelle, gene expression, and metabolic state that is executed dynamically as cells undergo the initiation, maintenance, and exit from quiescence. We review these major transitions, our current understanding of their molecular bases, and highlight unresolved questions. We summarize the primary methods employed for quiescence studies in yeast and discuss their relative merits. Understanding cell quiescence has important consequences for human disease as quiescent single-celled microbes are notoriously difficult to kill and quiescent human cells play important roles in diseases such as cancer. We argue that research on cellular quiescence will be accelerated through the adoption of common criteria, and methods, for defining cell quiescence. An integrated approach to studying cell quiescence, and a focus on the behavior of individual cells, will yield new insights into the pathways and processes that underlie cell quiescence leading to a more complete understanding of the life cycle of cells. TAKE AWAY: Quiescent cells are viable cells that have reversibly exited the cell cycle Quiescence is induced in response to a variety of nutrient starvation signals Quiescence is executed dynamically through three phases: initiation, maintenance, and exit Quiescence entails large-scale remodeling of gene expression, organelles, and metabolism Single-cell approaches are required to address heterogeneity among quiescent cells.

GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions

Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.

Capturing the transcription factor interactome in response to sub-lethal insecticide exposure

The increasing levels of pesticide resistance in agricultural pests and disease vectors represents a threat to both food security and global health. As insecticide resistance intensity strengthens and spreads, the likelihood of a pest encountering a sub-lethal dose of pesticide dramatically increases. Here, we apply dynamic Bayesian networks to a transcriptome time-course generated using sub-lethal pyrethroid exposure on a highly resistant Anopheles coluzzii population. The model accounts for circadian rhythm and ageing effects allowing high confidence identification of transcription factors with key roles in pesticide response. The associations generated by this model show high concordance with lab-based validation and identifies 44 transcription factors putatively regulating insecticide-responsive transcripts. We identify six key regulators, with each displaying differing enrichment terms, demonstrating the complexity of pesticide response. The considerable overlap of resistance mechanisms in agricultural pests and disease vectors strongly suggests that these findings are relevant in a wide variety of pest species.

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

Single-Cell Technologies to Understand the Mechanisms of Cellular Adaptation in Chemostats

There is a growing interest in continuous manufacturing within the bioprocessing community. In this context, the chemostat process is an important unit operation. The current application of chemostat processes in industry is limited although many high yielding processes are reported in literature. In order to reach the full potential of the chemostat in continuous manufacture, the output should be constant. However, adaptation is often observed resulting in changed productivities over time. The observed adaptation can be coupled to the selective pressure of the nutrient-limited environment in the chemostat. We argue that population heterogeneity should be taken into account when studying adaptation in the chemostat. We propose to investigate adaptation at the single-cell level and discuss the potential of different single-cell technologies, which could be used to increase the understanding of the phenomena. Currently, none of the discussed single-cell technologies fulfill all our criteria but in combination they may reveal important information, which can be used to understand and potentially control the adaptation.

Molecular and evolutionary processes generating variation in gene expression

Heritable variation in gene expression is common within and between species. This variation arises from mutations that alter the form or function of molecular gene regulatory networks that are then filtered by natural selection. High-throughput methods for introducing mutations and characterizing their cis- and trans-regulatory effects on gene expression (particularly, transcription) are revealing how different molecular mechanisms generate regulatory variation, and studies comparing these mutational effects with variation seen in the wild are teasing apart the role of neutral and non-neutral evolutionary processes. This integration of molecular and evolutionary biology allows us to understand how the variation in gene expression we see today came to be and to predict how it is most likely to evolve in the future.

Advances and challenges in single-cell RNA-seq of microbial communities

Microbes have developed complex strategies to respond to their environment and escape the immune system by individualizing their behavior. While single-cell RNA sequencing has become instrumental for studying mammalian cells, its use with fungi, protozoa and bacteria is still in its infancy. In this review, we highlight the major progress towards mapping the molecular states of microbes at the single-cell level using genome-wide transcriptomics and describe how these technologies can be extended to probe thousands of species at high throughput. We envision that mammalian and microbial single-cell profiling could soon be integrated for the study of microbial communities in health and disease.

Gene Regulatory Networks of Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 Inferred by a Computational Biology Approach

Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 are well-known cellulase fungal producers. However, few studies addressing global mechanisms for gene regulation of these two important organisms are available so far. A recent finding that the 2HH wild-type is closely related to P. oxalicum leads to a combined study of these two species. Firstly, we provide a global gene regulatory network for P. echinulatum 2HH and P. oxalicum 114-2, based on TF-TG orthology relationships, considering three related species with well-known regulatory interactions combined with TFBSs prediction. The network was then analyzed in terms of topology, identifying TFs as hubs, and modules. Based on this approach, we explore numerous identified modules, such as the expression of cellulolytic and xylanolytic systems, where XlnR plays a key role in positive regulation of the xylanolytic system. It also regulates positively the cellulolytic system by acting indirectly through the cellodextrin induction system. This remarkable finding suggests that the XlnR-dependent cellulolytic and xylanolytic regulatory systems are probably conserved in both P. echinulatum and P. oxalicum. Finally, we explore the functional congruency on the genes clustered in terms of communities, where the genes related to cellular nitrogen, compound metabolic process and macromolecule metabolic process were the most abundant. Therefore, our approach allows us to confer a degree of accuracy regarding the existence of each inferred interaction.

Joint learning of multiple gene networks from single-cell gene expression data

Inferring gene networks from gene expression data is important for understanding functional organizations within cells. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is possible to infer gene networks at single cell level. However, due to the characteristics of scRNA-seq data, such as cellular heterogeneity and high sparsity caused by dropout events, traditional network inference methods may not be suitable for scRNA-seq data. In this study, we introduce a novel joint Gaussian copula graphical model (JGCGM) to jointly estimate multiple gene networks for multiple cell subgroups from scRNA-seq data. Our model can deal with non-Gaussian data with missing values, and identify the common and unique network structures of multiple cell subgroups, which is suitable for scRNA-seq data. Extensive experiments on synthetic data demonstrate that our proposed model outperforms other compared state-of-the-art network inference models. We apply our model to real scRNA-seq data sets to infer gene networks of different cell subgroups. Hub genes in the estimated gene networks are found to be biological significance.

Random Parametric Perturbations of Gene Regulatory Circuit Uncover State Transitions in Cell Cycle

Many biological processes involve precise cellular state transitions controlled by complex gene regulation. Here, we use budding yeast cell cycle as a model system and explore how a gene regulatory circuit encodes essential information of state transitions. We present a generalized random circuit perturbation method for circuits containing heterogeneous regulation types and its usage to analyze both steady and oscillatory states from an ensemble of circuit models with random kinetic parameters. The stable steady states form robust clusters with a circular structure that are associated with cell cycle phases. This circular structure in the clusters is consistent with single-cell RNA sequencing data. The oscillatory states specify the irreversible state transitions along cell cycle progression. Furthermore, we identify possible mechanisms to understand the irreversible state transitions from the steady states. We expect this approach to be robust and generally applicable to unbiasedly predict dynamical transitions of a gene regulatory circuit.

Homologous Recombination: A GRAS Yeast Genome Editing Tool

The fermentation industry is known to be very conservative, relying on traditional yeast management. Yet, in the modern fast-paced world, change comes about in facets such as climate change altering the quality and quantity of harvests, changes due to government regulations e.g., the use of pesticides or SO2, the need to become more sustainable, and of course by changes in consumer preferences. As a silent companion of the fermentation industry, the wine yeast Saccharomyces cerevisiae has followed mankind through millennia, changing from a Kulturfolger, into a domesticated species for the production of bread, beer, and wine and further on into a platform strain for the production of biofuels, enzymes, flavors, or pharmaceuticals. This success story is based on the ‘awesome power of yeast genetics’. Central to this is the very efficient homologous recombination (HR) machinery of S. cerevisiae that allows highly-specific genome edits. This microsurgery tool is so reliable that yeast has put a generally recognized as safe (GRAS) label onto itself and entrusted to itself the life-changing decision of mating type-switching. Later, yeast became its own genome editor, interpreted as domestication events, to adapt to harsh fermentation conditions. In biotechnology, yeast HR has been used with tremendous success over the last 40 years. Here we discuss several types of yeast genome edits then focus on HR and its inherent potential for evolving novel wine yeast strains and styles relevant for changing markets.

A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast

Current methods for single-cell RNA sequencing (scRNA-seq) of yeast cells do not match the throughput and relative simplicity of the state-of-the-art techniques that are available for mammalian cells. In this study, we report how 10x Genomics' droplet-based single-cell RNA sequencing technology can be modified to allow analysis of yeast cells. The protocol, which is based on in-droplet spheroplasting of the cells, yields an order-of-magnitude higher throughput in comparison to existing methods. After extensive validation of the method, we demonstrate its use by studying the dynamics of the response of isogenic yeast populations to a shift in carbon source, revealing the heterogeneity and underlying molecular processes during this shift. The method we describe opens new avenues for studies focusing on yeast cells, as well as other cells with a degradable cell wall.

A Bright IDEA

Transcription factors (TFs) control the rate of mRNA production. Technological advances have made the task of measuring mRNA levels for all genes straightforward, but identifying causal relationships between TFs and their target genes remains an unsolved problem in biology. In their recent study, McIsaac and colleagues (Hackett et al, 2020) apply a method for inducing the overexpression of a TF and studying the dynamics with which all transcripts respond. Using time series analysis, they are able to resolve direct effects of TFs from secondary effects. This new experimental and analytical approach provides an efficient means of defining gene regulatory relationships for all TFs.

The single-cell eQTLGen consortium

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

Yeast Single-cell RNA-seq, Cell by Cell and Step by Step

Single-cell RNA-seq (scRNA-seq) has become an established method for uncovering the intrinsic complexity within populations. Even within seemingly homogenous populations of isogenic yeast cells, there is a high degree of heterogeneity that originates from a compact and pervasively transcribed genome. Research with microorganisms such as yeast represents a major challenge for single-cell transcriptomics, due to their small size, rigid cell wall, and low RNA content per cell. Because of these technical challenges, yeast-specific scRNA-seq methodologies have recently started to appear, each one of them relying on different cell-isolation and library-preparation methods. Consequently, each approach harbors unique strengths and weaknesses that need to be considered. We have recently developed a yeast single-cell RNA-seq protocol (yscRNA-seq), which is inexpensive, high-throughput and easy-to-implement, tailored to the unique needs of yeast. yscRNA-seq provides a unique platform that combines single-cell phenotyping via index sorting with the incorporation of unique molecule identifiers on transcripts that allows to digitally count the number of molecules in a strand- and isoform-specific manner. Here, we provide a detailed, step-by-step description of the experimental and computational steps of yscRNA-seq protocol. This protocol will ease the implementation of yscRNA-seq in other laboratories and provide guidelines for the development of novel technologies.

Network modeling of single-cell omics data: challenges, opportunities, and progresses

Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell-cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.