Single-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis

Single-cell RNA sequencing is a powerful technique that continues to expand across various biological applications. However, incomplete 3′-UTR annotations can impede single-cell analysis resulting in genes that are partially or completely uncounted. Performing single-cell RNA sequencing with incomplete 3′-UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single-cell isoform sequencing in tandem with single-cell RNA sequencing can rapidly improve 3′-UTR annotations. Using threespine stickleback fish (Gasterosteus aculeatus), we show that gene models resulting from a minimal embryonic single-cell isoform sequencing dataset retained 26.1% greater single-cell RNA sequencing reads than gene models from Ensembl alone. Furthermore, pooling our single-cell sequencing isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the single-cell isoform sequencing only dataset. In addition, isoforms identified by single-cell isoform sequencing included thousands of new splicing variants. The improved gene models obtained using single-cell isoform sequencing led to successful identification of cell types and increased the reads identified of many genes in our single-cell RNA sequencing stickleback dataset. Our work illuminates single-cell isoform sequencing as a cost-effective and efficient mechanism to rapidly annotate genomes for single-cell RNA sequencing.     

Single-cell transcriptome profiling reveals intratumoural heterogeneity and malignant progression in retinoblastoma

Retinoblastoma is a childhood retinal tumour that is the most common primary malignant intraocular tumour. However, it has been challenging to identify the cell types associated with genetic complexity. Here, we performed single-cell RNA sequencing on 14,739 cells from two retinoblastoma samples to delineate the heterogeneity and the underlying mechanism of retinoblastoma progression. Using a multiresolution network-based analysis, we identified two major cell types in human retinoblastoma. Cell trajectory analysis yielded a total of 5 cell states organized into two main branches, and the cell cycle-associated cone precursors were the cells of origin of retinoblastoma that were required for initiating the differentiation and malignancy process of retinoblastoma. Tumour cells differentiation reprogramming trajectory analysis revealed that cell-type components of multiple tumour-related pathways and predominantly expressed UBE2C were associated with an activation state in the malignant progression of the tumour, providing a potential novel “switch gene” marker during early critical stages in human retinoblastoma development. Thus, our findings improve our current understanding of the mechanism of retinoblastoma progression and are potentially valuable in providing novel prognostic markers for retinoblastoma.Subject terms: Eye cancer, Differentiation, Prognostic markers     

Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data

Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms. However, the noise inherent in single-cell RNA-sequencing data severely disturbs the accuracy of cell clustering, marker identification and visualization. We propose that clustering based on feature density profiles can distinguish informative features from noise. We named such strategy as ‘entropy subspace’ separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the ‘entropy subspace’ separation strategy with a consensus clustering method. We demonstrate that ENCORE performs superiorly on cell clustering and generates high-resolution visualization across 12 standard datasets. More importantly, ENCORE enables identification of group markers with biological significance from a hard-to-separate dataset. With the advantages of effective feature selection, improved clustering, accurate marker identification and high-resolution visualization, we present ENCORE to the community as an important tool for scRNA-seq data analysis to study cellular heterogeneity and discover group markers.     

Single-Cell Microbiology: Tools, Technologies, and Applications

The field of microbiology has traditionally been concerned with and focused on studies at the population level. Information on how cells respond to their environment, interact with each other, or undergo complex processes such as cellular differentiation or gene expression has been obtained mostly by inference from population-level data. Individual microorganisms, even those in supposedly “clonal” populations, may differ widely from each other in terms of their genetic composition, physiology, biochemistry, or behavior. This genetic and phenotypic heterogeneity has important practical consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. New appreciation of the importance of cellular heterogeneity, coupled with recent advances in technology, has driven the development of new tools and techniques for the study of individual microbial cells. Because observations made at the single-cell level are not subject to the “averaging” effects characteristic of bulk-phase, population-level methods, they offer the unique capacity to observe discrete microbiological phenomena unavailable using traditional approaches. As a result, scientists have been able to characterize microorganisms, their activities, and their interactions at unprecedented levels of detail.     

Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell.     

Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution

Phenotypic profiling of natural, engineered or synthetic cells has increasingly become a bottleneck in the mining and engineering of cell factories. Single-cell phenotyping technologies are highly promising for tackling this hurdle, yet ideally they should allow non-invasive live-cell probing, be label-free, provide landscape-like phenotyping capability, distinguish complex functions, operate with high speed, sufficient throughput and low cost, and finally, couple with cell sorting so as to enable downstream omics analysis. This review focuses on recent progress in Ramanome Technology Platform (RTP), which consists of Raman spectroscopy based phenotyping, sorting and sequencing of single cells, and discuss the key challenges and emerging trends. In addition, we propose ramanome, a collection of single-cell Raman spectra (SCRS) acquired from individual cells within a cellular population or consortium, as a new type of biological phenome datatype at the single-cell resolution. By establishing the phenome-genome links in a label-free, single-cell manner, RTP should find wide applications in functional screening and strain development of live microbial, plant and animal cell factories.     

Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method,reveals non-genetic gene-expression heterogeneity

Development of a highly reproducible and sensitive single-cell RNA sequencing (RNA-seq) method would facilitate the understanding of the biological roles and underlying mechanisms of non-genetic cellular heterogeneity. In this study, we report a novel single-cell RNA-seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity than existing methods. We show that single-cell Quartz-Seq can quantitatively detect various kinds of non-genetic cellular heterogeneity, and can detect different cell types and different cell-cycle phases of a single cell type. Moreover, this method can comprehensively reveal gene-expression heterogeneity between single cells of the same cell type in the same cell-cycle phase.     

From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of “supercell statistics”, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet''s disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet''s disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8⁺ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8⁺ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.     

SCOPE-Seq: a scalable technology for linking live cell imaging and single-cell RNA sequencing

Optically decodable beads link the identity of a sample to a measurement through an optical barcode, enabling libraries of biomolecules to be captured on beads in solution and decoded by fluorescence. This approach has been foundational to microarray, sequencing, and flow-based expression profiling technologies. We combine microfluidics with optically decodable beads and show that phenotypic analysis of living cells can be linked to single-cell sequencing. As a proof-of-concept, we demonstrate the accuracy and scalability of our tool called Single Cell Optical Phenotyping and Expression sequencing (SCOPE-Seq) to combine live cell imaging with single-cell RNA sequencing.     

Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.     

Direct Mapping from Intracellular Chemotaxis Signaling to Single-Cell Swimming Behavior

Bacterial chemotaxis allows bacteria to sense the chemical environment and modulate their swimming behavior accordingly. Although the intracellular chemotaxis signaling pathway has been studied extensively, experimental studies are still lacking that could provide direct link from the pathway output (the intracellular concentration of the phosphorylated form of the response regulator phosphorylated CheY (CheY-P)) to single-cell swimming behavior. Here, we measured the swimming behavior of individual Escherichia coli cells while simultaneously detecting the intracellular CheY-P concentration, thereby providing a direct relationship between the intracellular CheY-P concentration and the single-cell run-and-tumble behavior. The measured relationship is consistent with the ultrasensitivity of the motor switch and a “veto model” that describes the interaction among individual flagella, although contribution from the voting mechanism could not be ruled out.