scRNA-seq Analysis of Hemocytes of Penaeid Shrimp Under Virus Infection.

The classification of cells in non-model organisms has lagged behind the classification of cells in model organisms that have established cluster of differentiation marker sets. To reduce fish diseases, research is needed to better understand immune-related cells, or hemocytes, in non-model organisms like shrimp and other marine invertebrates. In this study, we used Drop-seq to examine how virus infection affected the populations of hemocytes in kuruma shrimp, Penaeus japonicus, which had been artificially infected with a virus. The findings demonstrated that virus infection reduced particular cell populations in circulating hemolymph and inhibited the expression of antimicrobial peptides. We also identified the gene sets that are likely to be responsible for this reduction. Additionally, we identified functionally unknown genes as novel antimicrobial peptides, and we supported this assumption by the fact that these genes were expressed in the population of hemocytes that expressed other antimicrobial peptides. In addition, we aimed to improve the operability of the experiment by conducting Drop-seq with fixed cells as a source and discussed the impact of methanol fixation on Drop-seq data in comparison to previous results obtained without fixation. These results not only deepen our understanding of the immune system of crustaceans but also demonstrate that single-cell analysis can accelerate research on non-model organisms.

Improved protocol for single-nucleus RNA-sequencing of frozen human bladder tumor biopsies.

This paper provides a laboratory workflow for single-nucleus RNA-sequencing (snRNA-seq) including a protocol for gentle nuclei isolation from fresh frozen tumor biopsies, making it possible to analyze biobanked material. To develop this protocol, we used non-frozen and frozen human bladder tumors and cell lines. We tested different lysis buffers (IgePal and Nuclei EZ) and incubation times in combination with different approaches for tissue and cell dissection: sectioning, semi-automated dissociation, manual dissociation with pestles, and semi-automated dissociation combined with manual dissociation with pestles. Our results showed that a combination of IgePal lysis buffer, tissue dissection by sectioning, and short incubation time was the best conditions for gentle nuclei isolation applicable for snRNA-seq, and we found limited confounding transcriptomic changes based on the isolation procedure. This protocol makes it possible to analyze biobanked material from patients with well-described clinical and histopathological information and known clinical outcomes with snRNA-seq.

scRNA-seq for Microcephaly Research [I]: Single-Cell Droplet Encapsulation, mRNA Capture, and cDNA Synthesis.

Single-cell RNA sequencing (scRNA-seq) allows for the transcriptomic profiling of a sample tissue with single-cell resolution. The concept of scRNA-seq builds on traditional, "bulk" RNA-seq by recording and preserving the cellular origin of each transcript throughout library preparation. Here we describe an adaptation of the Drop-Seq method (Macosko et al. Cell 161, 1202-1214, 2015), in which nanoliter-scale droplets are used to physically separate dissociated cells, while a cell-specific DNA barcode is simultaneously introduced. Following barcoding, cDNAs can be mixed and pooled while retaining the identity of the cell of origin. The benefit of the Drop-Seq approach is high throughput from relatively small samples of tissue. The method described here is appropriate for processing an input of as few as 150,000 cells, with a final yield of as many as 5000 single-cell transcripts captured.

scRNA-seq for Microcephaly Research [II]: Preparation of Single-Cell cDNA Libraries.

Droplet-based single-cell RNA-seq (scRNA-seq) requires the addition of bar codes that mark the cell and transcript of origin. Cell-specific bar codes, typically added by cDNA elongation on beads, label each transcript derived from an individual cell. Transcript-specific bar codes serve as unique molecular identifiers and allow for the maintenance of transcript proportions after PCR-based amplification of cDNA. This chapter provides methods for generating bar-coded scRNA-seq libraries after droplet encapsulation.

The effect of methanol fixation on single-cell RNA sequencing data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-sEq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome. RESULTS: We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result. CONCLUSIONS: Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.

Single nucleus transcriptomics data integration recapitulates the major cell types in human liver.

AIM: The aim of this study was to explore the benefits of data integration from different platforms for single nucleus transcriptomics profiling to characterize cell populations in human liver. METHODS: We generated single-nucleus RNA sequencing data from Chromium 10X Genomics and Drop-seq for a human liver sample. We utilized state of the art bioinformatics tools to undertake a rigorous quality control and to integrate the data into a common space summarizing the gene expression variation from the respective platforms, while accounting for known and unknown confounding factors. RESULTS: Analysis of single nuclei transcriptomes from both 10X and Drop-seq allowed identification of the major liver cell types, while the integrated set obtained enough statistical power to separate a small population of inactive hepatic stellate cells that was not characterized in either of the platforms. CONCLUSIONS: Integration of droplet-based single nucleus transcriptomics data enabled identification of a small cluster of inactive hepatic stellate cells that highlights the potential of our approach. We suggest single-nucleus RNA sequencing integrative approaches could be utilized to design larger and cost-effective studies.

Single-Cell Transcriptome Analysis of Colon Cancer Cell Response to 5-Fluorouracil-Induced DNA Damage.

DNA damage often induces heterogeneous cell-fate responses, such as cell-cycle arrest and apoptosis. Through single-cell RNA sequencing (scRNA-seq), we characterize the transcriptome response of cultured colon cancer cell lines to 5-fluorouracil (5FU)-induced DNA damage. After 5FU treatment, a single population of colon cancer cells adopts three distinct transcriptome phenotypes, which correspond to diversified cell-fate responses: apoptosis, cell-cycle checkpoint, and stress resistance. Although some genes are regulated uniformly across all groups of cells, many genes showed group-specific expression patterns mediating DNA damage responses specific to the corresponding cell fate. Some of these observations are reproduced at the protein level by flow cytometry and are replicated in cells treated with other 5FU-unrelated genotoxic drugs, camptothecin and etoposide. This work provides a resource for understanding heterogeneous DNA damage responses involving fractional killing and chemoresistance, which are among the major challenges in current cancer chemotherapy.

Single-cell RNA-seq analysis of the brainstem of mutant SOD1 mice reveals perturbed cell types and pathways of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease in which motor neurons throughout the brain and spinal cord progressively degenerate resulting in muscle atrophy, paralysis and death. Recent studies using animal models of ALS implicate multiple cell-types (e.g., astrocytes and microglia) in ALS pathogenesis in the spinal motor systems. To ascertain cellular vulnerability and cell-type specific mechanisms of ALS in the brainstem that orchestrates oral-motor functions, we conducted parallel single cell RNA sequencing (scRNA-seq) analysis using the high-throughput Drop-seq method. We isolated 1894 and 3199 cells from the brainstem of wildtype and mutant SOD1 symptomatic mice respectively, at postnatal day 100. We recovered major known cell types and neuronal subpopulations, such as interneurons and motor neurons, and trigeminal ganglion (TG) peripheral sensory neurons, as well as, previously uncharacterized interneuron subtypes. We found that the majority of the cell types displayed transcriptomic alterations in ALS mice. Differentially expressed genes (DEGs) of individual cell populations revealed cell-type specific alterations in numerous pathways, including previously known ALS pathways such as inflammation (in microglia), stress response (ependymal and an uncharacterized cell population), neurogenesis (astrocytes, oligodendrocytes, neurons), synapse organization and transmission (microglia, oligodendrocyte precursor cells, and neuronal subtypes), and mitochondrial function (uncharacterized cell populations). Other cell-type specific processes altered in SOD1 mutant brainstem include those from motor neurons (axon regeneration, voltage-gated sodium and potassium channels underlying excitability, potassium ion transport), trigeminal sensory neurons (detection of temperature stimulus involved in sensory perception), and cellular response to toxic substances (uncharacterized cell populations). DEGs consistently altered across cell types (e.g., Malat1), as well as cell-type specific DEGs, were identified. Importantly, DEGs from various cell types overlapped with known ALS genes from the literature and with top hits from an existing human ALS genome-wide association study (GWAS), implicating the potential cell types in which the ALS genes function with ALS pathogenesis. Our molecular investigation at single cell resolution provides comprehensive insights into the cell types, genes and pathways altered in the brainstem in a widely used ALS mouse model.

Selective Retrieval of Individual Cells from Microfluidic Arrays Combining Dielectrophoretic Force and Directed Hydrodynamic Flow.

Hydrodynamic-based microfluidic platforms enable single-cell arraying and analysis over time. Despite the advantages of established microfluidic systems, long-term analysis and proliferation of cells selected in such devices require off-chip recovery of cells as well as an investigation of on-chip analysis on cell phenotype, requirements still largely unmet. Here, we introduce a device for single-cell isolation, selective retrieval and off-chip recovery. To this end, singularly addressable three-dimensional electrodes are embedded within a microfluidic channel, allowing the selective release of single cells from their trapping site through application of a negative dielectrophoretic (DEP) force. Selective capture and release are carried out in standard culture medium and cells can be subsequently mitigated towards a recovery well using micro-engineered hybrid SU-8/PDMS pneumatic valves. Importantly, transcriptional analysis of recovered cells revealed only marginal alteration of their molecular profile upon DEP application, underscored by minor transcriptional changes induced upon injection into the microfluidic device. Therefore, the established microfluidic system combining targeted DEP manipulation with downstream hydrodynamic coordination of single cells provides a powerful means to handle and manipulate individual cells within one device.

Evaluation of STAR and Kallisto on Single Cell RNA-Seq Data Alignment.

Alignment of scRNA-Seq data are the first and one of the most critical steps of the scRNA-Seq analysis workflow, and thus the choice of proper aligners is of paramount importance. Recently, STAR an alignment method and Kallisto a pseudoalignment method have both gained a vast amount of popularity in the single cell sequencing field. However, an unbiased third-party comparison of these two methods in scRNA-Seq is lacking. Here we conduct a systematic comparison of them on a variety of Drop-seq, Fluidigm and 10x genomics data, from the aspects of gene abundance, alignment accuracy, as well as computational speed and memory use. We observe that STAR globally produces more genes and higher gene-expression values, compared to Kallisto, as well as Bowtie2, another popular alignment method for bulk RNA-Seq. STAR also yields higher correlations of the Gini index for the genes with RNA-FISH validation results. Using 10x genomics PBMC 3K scRNA-Seq and mouse cortex single nuclei RNA-Seq data, STAR shows similar or better cell-type annotation results, by detecting a larger subset of known gene markers. However, the gain of accuracy and gene abundance of STAR alignment comes with the price of significantly slower computation time (4 folds) and more memory (7.7 folds), compared to Kallisto.

Analysis of Transcriptional Profiling of Immune Cells at the Single-Cell Level.

RNA sequencing has proven to be a key innovation for the study of biological processes by enabling scientists to measure differences in gene expression in different tissues.With recent advances in sequencing technology, researchers are able to measure gene transcription at the single-cell level, revealing previously unknown diversity and specificity of immune cells. The single-cell sequencing method now enables profiling of the T-cell receptor (TCR) genes resulting from V(D)J recombination.Here we describe how to adapt single-cell RNA sequencing data generated using the 10× genomics 5'V(D)J immune cell profiling workflow for integration into the R analysis pipeline.We will start with the data matrix files generated from the 10× genomics Cell Ranger alignment software and detail how to format this data as input for the R analysis package called Seurat such that data from both the overall cell transcript abundance and the targeted V(D)J transcript abundance data can be visualized on the same plots.

Integration of Flow Cytometry and Single Cell Sequencing.

Integrating cytometric analysis of cells, mitochondria, and other polynucleotide-containing biological particles with high-throughput single particle sequencing would provide an ultimate bioanalytical tool, simultaneously assessing phenotype, functionality, genome, and transcriptome of each particle in a large population. Here, we describe how such integration could be performed by adapting existing, well-established technologies.

M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data.

BACKGROUND: Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. RESULTS: We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. CONCLUSION: A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

Single cell RNA-seq in the sea urchin embryo show marked cell-type specificity in the Delta/Notch pathway.

Sea urchin embryos are excellent for in vivo functional studies because of their transparency and tractability in manipulation. They are also favorites for pharmacological approaches since they develop in an aquatic environment and addition of test substances is straightforward. A concern in many pharmacological tests though is the potential for pleiotropic effects that confound the conclusions drawn from the results. Precise cellular interpretations are often not feasible because the impact of the perturbant is not known. Here we use single-cell mRNA (messenger RNA) sequencing as a metric of cell types in the embryo and to determine the selectivity of two commonly used inhibitors, one each for the Wnt and the Delta-Notch pathways, on these nascent cell types. We identified 11 distinct cell types based on mRNA profiling, and that the cell lineages affected by Wnt and Delta/Notch inhibition were distinct from each other. These data support specificity and distinct effects of these signaling pathways in the embryo and illuminate how these conserved pathways selectively regulate cell lineages at a single cell level. Overall, we conclude that single cell RNA-seq analysis in this embryo is revealing of the cell types present during development, of the changes in the gene regulatory network resulting from inhibition of various signaling pathways, and of the selectivity of these pathways in influencing developmental trajectories.

Dean Flow Assisted Single Cell and Bead Encapsulation for High Performance Single Cell Expression Profiling.

Droplet microfluidics-based platform (Drop-seq) has been shown to be a powerful tool for single cell expression profiling. Nevertheless, this platform required the simultaneous encapsulation of single cell and single barcoded bead, the incidence of which was very low, limiting its efficiency. Spiral channels were reported to focus the barcoded beads and thus increased the efficiency, but focusing of cells was not demonstrated, which could potentially further enhance the performance. Here, we designed spiral and serpentine channels to focus both bead and cell solutions and implemented this microfluidic design on Drop-seq. We characterized the effect of cell/bead concentration on encapsulation results and tested the performance by coencapsulating barcoded beads and human-mouse cell mixtures followed by sequencing. The results showed ∼300% and ∼40% increase in cell utilization rate compared to the traditional Drop-seq device and the device focusing beads alone, respectively. This chip design showed great potential for high efficiency single cell expression profiling.

Single-Cell RNA Sequencing with Drop-Seq.

Drop-Seq is a low-cost, high-throughput platform to profile thousands of cells by encapsualting them into individual droplets. Uniquely barcoded mRNA capture microparticles and cells are coconfined through a microfluidic device within the droplets where they undergo cell lysis and RNA hybridiztion. After breaking the droplets and pooling the hybridized particles, reverse transcription, PCR, and sequencing in single reactions allow to generate data from thousands of single-cell transcriptomes while maintaining information on the cellular origin of each transcript.

Identifying gene expression from single cells to single genes.

Gene regulatory networks reveal how transcription factors contribute to a dynamic cascade of cellular information processing. Recent advances in technologies have enhanced the toolkit for testing GRN mechanisms and connections. Here we emphasize three approaches that we have found important for interrogating transcriptional mechanisms in echinoderms: single cell mRNA sequencing (drop-seq), nascent RNA detection and identification, and chromatin immunoprecipitation (ChIP). We present these applications in order since it is a logical experimental protocol. With preliminary information from bulk mRNA transcriptome analysis and differential gene expression studies (DE-seq), one may need to test in what specific cells important genes may be expressed and to use single cell sequencing to define such links. Nascent RNA analysis with the Click-iT chemistry allows the investigator to deduce when the RNA was transcribed, not just identify its presence, and ChIP allows the investigator to study direct interactions of putative transcriptional regulators with the gene promoter of interest. This flow of thinking, and the technologies to support it, is presented here for echinoderms. While many of the procedures are general and applicable to many organisms and cell types, we emphasize unique aspects of the protocols for consideration in using echinoderm embryos, larvae, and adult tissues.

Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key ß-Cell-Specific Disease Genes.

Identification of human disease signature genes typically requires samples from many donors to achieve statistical significance. Here, we show that single-cell heterogeneity analysis may overcome this hurdle by significantly improving the test sensitivity. We analyzed the transcriptome of 39,905 single islets cells from 9 donors and observed distinct ß cell heterogeneity trajectories associated with obesity or type 2 diabetes (T2D). We therefore developed RePACT, a sensitive single-cell analysis algorithm to identify both common and specific signature genes for obesity and T2D. We mapped both ß-cell-specific genes and disease signature genes to the insulin regulatory network identified from a genome-wide CRISPR screen. Our integrative analysis discovered the previously unrecognized roles of the cohesin loading complex and the NuA4/Tip60 histone acetyltransferase complex in regulating insulin transcription and release. Our study demonstrated the power of combining single-cell heterogeneity analysis and functional genomics to dissect the etiology of complex diseases.

Better Being Single? Omics Improves Kidney Organoids.

CONTEXT: Human kidney organoids are complex structures resembling nephron arrays, which can be derived in a variety of ways. Whether all of these differentiation protocols produce qualitatively similar organoid cell types is not yet clear. Subject of Review: A comparative analysis of 2 organoid differentiation protocols is recently reported in Cell Stem Cell [Wu et al.: Cell Stem Cell 2018;pii:S1934-5909(18)30491-0], using single cell RNA sequencing (scRNA-seq) as an analytical tool. This demonstrates that the 2 protocols have much in common, and that neither produces kidney cells in a pure or comprehensive manner. Ureteric lineages appear to be absent, and organoids are contaminated with non-kidney cell types, including neurons and muscle cells. Based on the scRNA-seq datasets, a new differentiation protocol is devised to reduce non-kidney cell types, without adversely affecting organoid epithelial cells. Second Opinion: Together with published analyses of a third differentiation protocol, these findings suggest more commonalities than differences between kidney organoid platforms, and identify critical strategies for functional improvement of these cellular structures.

Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems.

Since its establishment in 2009, single-cell RNA sequencing (RNA-seq) has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to profile single cells confers particular benefits. Although most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, with the same cell line and bioinformatics pipeline, we developed directly comparable datasets for each of three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. Although each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.