Quantitative biology of single neurons

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence

Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.

Single-Cell Transcriptomics Reveal a Correlation between Genome Architecture and Gene Family Evolution in Ciliates

Ciliates, a eukaryotic clade that is over 1 billion years old, are defined by division of genome function between transcriptionally inactive germline micronuclei and functional somatic macronuclei. To date, most analyses of gene family evolution have been limited to cultivable model lineages (e.g., Tetrahymena, Paramecium, Oxytricha, and Stylonychia). Here, we focus on the uncultivable Karyorelictea and its understudied sister class Heterotrichea, which represent two extremes in genome architecture. Somatic macronuclei within the Karyorelictea are described as nearly diploid, while the Heterotrichea have hyperpolyploid somatic genomes. Previous analyses indicate that genome architecture impacts ciliate gene family evolution as the most diverse and largest gene families are found in lineages with extensively processed somatic genomes (i.e., possessing thousands of gene-sized chromosomes). To further assess ciliate gene family evolution, we analyzed 43 single-cell transcriptomes from 33 ciliate species representing 10 classes. Focusing on conserved eukaryotic genes, we use estimates of transcript diversity as a proxy for the number of paralogs in gene families among four focal clades: Karyorelictea, Heterotrichea, extensive fragmenters (with gene-size somatic chromosomes), and non-extensive fragmenters (with more traditional somatic chromosomes), the latter two within the subphylum Intramacronucleata. Our results show that (i) the Karyorelictea have the lowest average transcript diversity, while Heterotrichea are highest among the four groups; (ii) proteins in Karyorelictea are under the highest functional constraints, and the patterns of selection in ciliates may reflect genome architecture; and (iii) stop codon reassignments vary among members of the Heterotrichea and Spirotrichea but are conserved in other classes.IMPORTANCE To further our understanding of genome evolution in eukaryotes, we assess the relationship between patterns of molecular evolution within gene families and variable genome structures found among ciliates. We combine single-cell transcriptomics with bioinformatic tools, focusing on understudied and uncultivable lineages selected from across the ciliate tree of life. Our analyses show that genome architecture correlates with patterns of protein evolution as lineages with more canonical somatic genomes, such as the class Karyorelictea, have more conserved patterns of molecular evolution compared to other classes. This study showcases the power of single-cell transcriptomics for investigating genome architecture and evolution in uncultivable microbial lineages and provides transcriptomic resources for further research on genome evolution.

Single cell transcriptomics of noncoding RNAs and their cell-specificity

Recent developments of single cell transcriptome profiling methods have led to the realization that many seemingly homogeneous cells have surprising levels of expression variability. The biological implications of the high degree of variability is unclear but one possibility is that many genes are restricted in expression to small lineages of cells, suggesting the existence of many more cell types than previously estimated. Noncoding RNA (ncRNA) are thought to be key parts of gene regulatory processes and their single cell expression patterns may help to dissect the biological function of single cell variability. Technology for measuring ncRNA in single cell is still in development and most of the current single cell datasets have reliable measurements for only long noncoding RNA (lncRNA). Most works report that lncRNAs show lineage-specific restricted expression patterns, which suggest that they might determine, at least in part, lineage fates and cell subtypes. However, evidence is still inconclusive as to whether lncRNAs and other ncRNAs are more lineage-specific than protein-coding genes. Nevertheless, measurement of ncRNAs in single cells will be important for studies of cell types and single cell function. WIREs RNA 2017, 8:e1433. doi: 10.1002/wrna.1433 For further resources related to this article, please visit the WIREs website.

Cellular Deconstruction: Finding Meaning in Individual Cell Variation

The advent of single cell transcriptome analysis has permitted the discovery of cell-to-cell variation in transcriptome expression of even presumptively identical cells. We hypothesize that this variability reflects a many-to-one relation between transcriptome states and the phenotype of a cell. In this relation, the molecular ratios of the subsets of RNA are determined by the stoichiometric constraints of the cell systems, which underdetermine the transcriptome state. Furthermore, the variability is, in part, induced by the tissue context and is important for system-level function. This theory is analogous to theories of literary deconstruction, where multiple 'signifiers' work in opposition to one another to create meaning. By analogy, transcriptome phenotypes should be defined as subsets of RNAs comprising selected RNA systems where the system-associated RNAs are balanced with each other to produce the associated cellular function. This idea provides a framework for understanding cellular heterogeneity in phenotypic responses to variant conditions, such as disease challenge.

Making a big thing of a small cell--recent advances in single cell analysis

Single cell analysis is an emerging field requiring a high level interdisciplinary collaboration to provide detailed insights into the complex organisation, function and heterogeneity of life. This review is addressed to life science researchers as well as researchers developing novel technologies. It covers all aspects of the characterisation of single cells (with a special focus on mammalian cells) from morphology to genetics and different omics-techniques to physiological, mechanical and electrical methods. In recent years, tremendous advances have been achieved in all fields of single cell analysis: (1) improved spatial and temporal resolution of imaging techniques to enable the tracking of single molecule dynamics within single cells; (2) increased throughput to reveal unexpected heterogeneity between different individual cells raising the question what characterizes a cell type and what is just natural biological variation; and (3) emerging multimodal approaches trying to bring together information from complementary techniques paving the way for a deeper understanding of the complexity of biological processes. This review also covers the first successful translations of single cell analysis methods to diagnostic applications in the field of tumour research (especially circulating tumour cells), regenerative medicine, drug discovery and immunology.

Divergence of RNA localization between rat and mouse neurons reveals the potential for rapid brain evolution

Background

Neurons display a highly polarized architecture. Their ability to modify their features under intracellular and extracellular stimuli, known as synaptic plasticity, is a key component of the neurochemical basis of learning and memory. A key feature of synaptic plasticity involves the delivery of mRNAs to distinct sub-cellular domains where they are locally translated. Regulatory coordination of these spatio-temporal events is critical for synaptogenesis and synaptic plasticity as defects in these processes can lead to neurological diseases. In this work, using microdissected dendrites from primary cultures of hippocampal neurons of two mouse strains (C57BL/6 and Balb/c) and one rat strain (Sprague–Dawley), we investigate via microarrays, subcellular localization of mRNAs in dendrites of neurons to assay the evolutionary differences in subcellular dendritic transcripts localization.

Results

Our microarray analysis highlighted significantly greater evolutionary diversification of RNA localization in the dendritic transcriptomes (81% gene identity difference among the top 5% highly expressed genes) compared to the transcriptomes of 11 different central nervous system (CNS) and non-CNS tissues (average of 44% gene identity difference among the top 5% highly expressed genes). Differentially localized genes include many genes involved in CNS function.

Conclusions

Species differences in sub-cellular localization may reflect non-functional neutral drift. However, the functional categories of mRNA showing differential localization suggest that at least part of the divergence may reflect activity-dependent functional differences of neurons, mediated by species-specific RNA subcellular localization mechanisms.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-883) contains supplementary material, which is available to authorized users.

Experience and the ever-changing brain: what the transcriptome can reveal

The brain is an ever-changing organ that encodes memories and directs behavior. Neuroanatomical studies have revealed structural plasticity of neural architecture, and advances in gene expression technology and epigenetics have demonstrated new mechanisms underlying the brain's dynamic nature. Stressful experiences challenge the plasticity of the brain, and prolonged exposure to environmental stress redefines the normative transcriptional profile of both neurons and glia, and can lead to the onset of mental illness. A more thorough understanding of normal and abnormal gene expression is needed to define the diseased brain and improve current treatments for psychiatric disorders. The efforts to describe gene expression networks have been bolstered by microarray and RNA-sequencing technologies. The heterogeneity of neural cell populations and their unique microenvironments, coupled with broad ranging interconnectivity, makes resolving this complexity exceedingly challenging and requires the combined efforts of single cell and systems level expression profiling to identify targets for therapeutic intervention.

Single-cell RNA-seq: advances and future challenges

Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here—each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.

Atomic force microscopic detection enabling multiplexed low-cycle-number quantitative polymerase chain reaction for biomarker assays

Quantitative polymerase chain reaction is the current "golden standard" for quantification of nucleic acids; however, its utility is constrained by an inability to easily and reliably detect multiple targets in a single reaction. We have successfully overcome this problem with a novel combination of two widely used approaches: target-specific multiplex amplification with 15 cycles of polymerase chain reaction (PCR), followed by single-molecule detection of amplicons with atomic force microscopy (AFM). In test experiments comparing the relative expression of ten transcripts in two different human total RNA samples, we find good agreement between our single reaction, multiplexed PCR/AFM data, and data from 20 individual singleplex quantitative PCR reactions. This technique can be applied to virtually any analytical problem requiring sensitive measurement concentrations of multiple nucleic acid targets.

Developmental imaging: the avian embryo hatches to the challenge

The avian embryo provides a multifaceted model to study developmental mechanisms because of its accessibility to microsurgery, fluorescence cell labeling, in vivo imaging, and molecular manipulation. Early two-dimensional planar growth of the avian embryo mimics human development and provides unique access to complex cell migration patterns using light microscopy. Later developmental events continue to permit access to both light and other imaging modalities, making the avian embryo an excellent model for developmental imaging. For example, significant insights into cell and tissue behaviors within the primitive streak, craniofacial region, and cardiovascular and peripheral nervous systems have come from avian embryo studies. In this review, we provide an update to recent advances in embryo and tissue slice culture and imaging, fluorescence cell labeling, and gene profiling. We focus on how technical advances in the chick and quail provide a clearer understanding of how embryonic cell dynamics are beautifully choreographed in space and time to sculpt cells into functioning structures. We summarize how these technical advances help us to better understand basic developmental mechanisms that may lead to clinical research into human birth defects and tissue repair.

Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue

Transcriptome profiling of single cells resident in their natural microenvironment depends upon RNA capture methods that are both noninvasive and spatially precise. We engineered a transcriptome in vivo analysis (TIVA) tag, which upon photoactivation enables mRNA capture from single cells in live tissue. Using the TIVA tag in combination with RNA sequencing (RNA-seq), we analyzed transcriptome variance among single neurons in culture and in mouse and human tissue in vivo. Our data showed that the tissue microenvironment shapes the transcriptomic landscape of individual cells. The TIVA methodology is, to our knowledge, the first noninvasive approach for capturing mRNA from live single cells in their natural microenvironment.