Single-cell systems biology: probing the basic unit of information flow

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

Fire Burn and Cauldron Bubble: What Is in Your Genome Editing Brew?

Genome editing is a rapidly evolving biotechnology with the potential to transform many sectors of industry such as agriculture, biomanufacturing, and medicine. This technology is enabled by an ever-growing portfolio of biomolecular reagents that span the central dogma, from DNA to RNA to protein. In this paper, we draw from our unique perspective as the National Metrology Institute of the United States to bring attention to the importance of understanding and reporting genome editing formulations accurately and promoting concepts to verify successful delivery into cells. Achieving the correct understanding may be hindered by the way units, quantities, and stoichiometries are reported in the field. We highlight the variability in how editing formulations are reported in the literature and examine how a reference molecule could be used to verify the delivery of a reagent into cells. We provide recommendations on how more accurate reporting of editing formulations and more careful verification of the steps in an editing experiment can help set baseline expectations of reagent performance, toward the aim of enabling genome editing studies to be more reproducible. We conclude with a future outlook on technologies that can further our control and enable our understanding of genome editing outcomes at the single-cell level.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Quantifying information of intracellular signaling: progress with machine learning

Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

Single-Cell RNA Sequencing of In Vitro Expanded Chondrocytes: MSC-Like Cells With No Evidence of Distinct Subsets

OBJECTIVE

To investigate the heterogeneity of in vitro expanded chondrocytes used for autologous chondrocyte implantation.

METHODS

Human articular chondrocytes were expanded in vitro for 14 days, sorted into 86 single cells using fluorescence-activated cell sorting and subjected to single-cell RNA sequencing. Principal component, Cross R² hierarchical clustering, and differential gene expression analyses were used for data evaluation. Flow cytometry and single-cell RT-qPCR (reverse transcriptase quantitative polymerase chain reaction) was used to validate the results of the RNA sequencing data Polyclonal chondrocyte populations from the same donor were differentiated in vitro toward the osteogenic and adipogenic lineages.

RESULTS

There was considerable variation in gene expression between individual cells, but we found no evidence for separate cell subpopulations based on principal component, hierarchical clustering, and differential gene expression analysis. Most of the cells expressed all the markers defining mesenchymal stem cells, and as polyclonal chondrocyte populations from the same donor were shown to differentiate into osteocytes and adipocytes in vitro, these cells formally qualify as mesenchymal stem cells.

CONCLUSIONS

In vitro expanded chondrocytes consist of one single population of cells with heterogeneity in gene expression between the cells. Dedifferentiated chondrocytes qualify as mesenchymal stem cells as they fulfill all the criteria suggested by the International Society for Cellular Therapy.

Omics in Systems Biology: Current Progress and Future Outlook

Biological research has undergone tremendous changes over the past three decades. Research used to almost exclusively focus on a single aspect of a single molecule per experiment. Modern technologies have enabled thousands of molecules to be simultaneously analyzed and the way that these molecules influence each other to be discerned. The change is so dramatic that it has given rise to a whole new descriptive suffix (i.e., omics) to describe these fields of study. While genomics was arguably the initial driver of this new trend, it quickly spread to other biological entities resulting in the creation of transcriptomics, proteomics, metabolomics, etc. The development of these "big four omics" created a wave of other omic fields, such as epigenomics, glycomics, lipidomics, microbiomics, and even foodomics; all with the purpose of comprehensively studying all the molecular entities or processes within their respective domain. The large number of omic fields that are invented even led to the term "panomics" as a way to classify them all under one category. Ultimately, all of these omic fields are setting the foundation for developing systems biology; in which the focus will be on determining the complex interactions that occur within biological systems.

Single Cell Systems Analysis: Decision Geometry In Outliers

MOTIVATION

Anti-cancer therapeutics of the highest calibre currently focus on combinatorial targeting of specific oncoproteins and tumour suppressors. Clinical relapse depends upon intratumoral heterogeneity which serves as substrate variation during evolution of resistance to therapeutic regimens.

RESULTS

The present review advocates single cell systems biology as the optimal level of analysis for remediation of clinical relapse. Graph theory approaches to understanding decision-making in single cells may be abstracted one level further, to the geometry of decision-making in outlier cells, in order to define evolution-resistant cancer biomarkers. Systems biologists currently working with omics data are invited to consider phase portrait analysis as a mediator between graph theory and deep learning approaches. Perhaps counter-intuitively, the tangible clinical needs of cancer patients may depend upon the adoption of higher level mathematical abstractions of cancer biology.

SUPPLEMENTARY INFORMATION

supplementary data available at Bioinformatics online.

Monitoring the spatio-temporal organization and dynamics of the genome

The spatio-temporal organization of chromatin in the eukaryotic cell nucleus is of vital importance for transcription, DNA replication and genome maintenance. Each of these activities is tightly regulated in both time and space. While we have a good understanding of chromatin organization in space, for example in fixed snapshots as a result of techniques like FISH and Hi-C, little is known about chromatin dynamics in living cells. The rapid development of flexible genomic loci imaging approaches can address fundamental questions on chromatin dynamics in a range of model organisms. Moreover, it is now possible to visualize not only single genomic loci but the whole genome simultaneously. These advances have opened many doors leading to insight into several nuclear processes including transcription and DNA repair. In this review, we discuss new chromatin imaging methods and how they have been applied to study transcription.

mRNA Metabolism in Cardiac Development and Disease: Life After Transcription

The central dogma of molecular biology illustrates the importance of mRNAs as critical mediators between genetic information encoded at the DNA level and proteomes/metabolomes that determine the diverse functional outcome at the cellular and organ levels. Although the total number of protein-producing (coding) genes in the mammalian genome is ~20,000, it is evident that the intricate processes of cardiac development and the highly regulated physiological regulation in the normal heart, as well as the complex manifestation of pathological remodeling in a diseased heart, would require a much higher degree of complexity at the transcriptome level and beyond. Indeed, in addition to an extensive regulatory scheme implemented at the level of transcription, the complexity of transcript processing following transcription is dramatically increased. RNA processing includes post-transcriptional modification, alternative splicing, editing and transportation, ribosomal loading, and degradation. While transcriptional control of cardiac genes has been a major focus of investigation in recent decades, a great deal of progress has recently been made in our understanding of how post-transcriptional regulation of mRNA contributes to transcriptome complexity. In this review, we highlight some of the key molecular processes and major players in RNA maturation and post-transcriptional regulation. In addition, we provide an update to the recent progress made in the discovery of RNA processing regulators implicated in cardiac development and disease. While post-transcriptional modulation is a complex and challenging problem to study, recent technological advancements are paving the way for a new era of exciting discoveries and potential clinical translation in the context of cardiac biology and heart disease.

The variome concept: focus on CNVariome

Background

Variome may be used for designating complex system of interplay between genomic variations specific for an individual or a disease. Despite the recognized complexity of genomic basis for phenotypic traits and diseases, studies of genetic causes of a disease are usually dedicated to the identification of single causative genomic changes (mutations). When such an artificially simplified model is employed, genomic basis of phenotypic outcomes remains elusive in the overwhelming majority of human diseases. Moreover, it is repeatedly demonstrated that multiple genomic changes within an individual genome are likely to underlie the phenome. Probably the best example of cumulative effect of variome on the phenotype is CNV (copy number variation) burden. Accordingly, we have proposed a variome concept based on CNV studies providing the evidence for the existence of a CNVariome (the set of CNV affecting an individual genome), a target for genomic analyses useful for unraveling genetic mechanisms of diseases and phenotypic traits.

Conclusion

Variome (CNVariome) concept suggests that a genomic milieu is determined by the whole set of genomic variations (CNV) within an individual genome. The genomic milieu is likely to result from interplay between these variations. Furthermore, such kind of variome may be either individual or disease-specific. Additionally, such variome may be pathway-specific. The latter is able to affect molecular/cellular pathways of genome stability maintenance leading to occurrence of genomic/chromosome instability and/or somatic mosaicism resulting in somatic variome. This variome type seems to be important for unraveling disease mechanisms, as well. Finally, it appears that bioinformatic analysis of both individual and somatic variomes in the context of diseases- and pathway-specific variomes is the most promising way to determine genomic basis of the phenome and to unravel disease mechanisms for the management and treatment of currently incurable diseases.

Nuclear Localization of Huntingtin mRNA Is Specific to Cells of Neuronal Origin

Huntington's disease (HD) is a monogenic neurodegenerative disorder representing an ideal candidate for gene silencing with oligonucleotide therapeutics (i.e., antisense oligonucleotides [ASOs] and small interfering RNAs [siRNAs]). Using an ultra-sensitive branched fluorescence in situ hybridization (FISH) method, we show that ∼50% of wild-type HTT mRNA localizes to the nucleus and that its nuclear localization is observed only in neuronal cells. In mouse brain sections, we detect Htt mRNA predominantly in neurons, with a wide range of Htt foci observed per cell. We further show that siRNAs and ASOs efficiently eliminate cytoplasmic HTT mRNA and HTT protein, but only ASOs induce a partial but significant reduction of nuclear HTT mRNA. We speculate that, like other mRNAs, HTT mRNA subcellular localization might play a role in important neuronal regulatory mechanisms.

Challenges in measuring and understanding biological noise

Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.