Single Cell Analysis: From Technology to Biology and Medicine

XCVATR: detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples

BACKGROUND

RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states.

RESULTS

Here, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local "clumps" of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors.

CONCLUSIONS

XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR .

Development and Validation of a Competitive ELISA Based on Bovine Monoclonal Antibodies for the Detection of Neutralizing Antibodies against Foot-and-Mouth Disease Virus Serotype A

The level of neutralizing antibodies in vaccinated animals is directly related to their level of protection against a virus challenge. The virus neutralization test (VNT) is a "gold standard" method for detecting neutralizing antibodies against foot-and-mouth disease virus (FMDV). However, VNT requires high-containment facilities that can handle live viruses and is not suitable for large-scale serological surveillance. In this study, a bovine broadly neutralizing monoclonal antibody (W145) against FMDV serotype A was successfully produced using fluorescence-based single-B-cell antibody technology. Using biotinylated W145 as a detector antibody and another bovine cross-reactive monoclonal antibody, E32, which was produced previously as a capture antibody, a competitive enzyme-linked immunosorbent assay for the detection of neutralizing antibodies (NAC-ELISA) against FMDV serotype A was developed. The specificity and sensitivity of the assay were evaluated to be 99.04% and 100%, respectively. A statistically significant correlation (r = 0.9334, P < 0.0001) was observed between the NAC-ELISA titers and the VNT titers, suggesting that the NAC-ELISA could detect neutralizing antibodies against FMDV serotype A and could be used to evaluate protective immunity.

Real-time capture of single particles in controlled flow by a rapidly generated foci array with adjustable intensity and pattern

We propose a new, to the best of our knowledge, technique to capture single particles in real-time in a microfluidic system with controlled flow using micro-pillar traps fabricated by one-step. The micro pillars are fabricated in parallel by femtosecond multi-foci laser beams, which are generated by multiplexing gratings. As the generation process does not need integration loops, the pattern and the intensity distribution of the foci array can be controlled in real-time by changing the parameters of gratings. The real-time control of the foci array enables rapidly fabricating microtraps in the microchannel with adjustment of the pillar spaces and patterns according to the sizes and shapes of target particles. This technology provides an important step towards using platforms based on single-particle analysis, and it paves the way for the development of innovative microfluidic devices for single-cell analysis.

NeuroSystematics and Periodic System of Neurons: Model vs Reference Species at Single-Cell Resolution

There is more than one way to develop neuronal complexity, and animals frequently use different molecular toolkits to achieve similar functional outcomes (=convergent evolution). Neurons are different not only because they have different functions, but also because neurons and circuits have different genealogies, and perhaps independent origins at the broadest scale from ctenophores and cnidarians to cephalopods and primates. By combining modern phylogenomics, single-neuron sequencing (scRNA-seq), machine learning, single-cell proteomics, and metabolomic across Metazoa, it is possible to reconstruct the evolutionary histories of neurons tracing them to ancestral secretory cells. Comparative data suggest that neurons, and perhaps synapses, evolved at least 2-3 times (in ctenophore, cnidarian and bilateral lineages) during ∼600 million years of animal evolution. There were also several independent events of the nervous system centralization either from a common bilateral/cnidarian ancestor without the bona fide neurons or from the urbilaterian with diffuse, nerve-net type nervous system. From the evolutionary standpoint, (i) a neuron should be viewed as a functional rather than a genetic character, and (ii) any given neural system might be chimeric and composed of different cell lineages with distinct origins and evolutionary histories. The identification of distant neural homologies or examples of convergent evolution among 34 phyla will not only allow the reconstruction of neural systems' evolution but together with single-cell "omic" approaches the proposed synthesis would lead to the "Periodic System of Neurons" with predictive power for neuronal phenotypes and plasticity. Such a phylogenetic classification framework of Neuronal Systematics (NeuroSystematics) might be a conceptual analog of the Periodic System of Chemical Elements. scRNA-seq profiling of all neurons in an entire brain or Brain-seq is now fully achievable in many nontraditional reference species across the entire animal kingdom. Arguably, marine animals are the most suitable for the proposed tasks because the world oceans represent the greatest taxonomic and body-plan diversity.

Improved Vaccine against PRRSV: Current Progress and Future Perspective

Porcine reproductive and respiratory syndrome virus (PRRSV), one of the most economically significant pathogens worldwide, has caused numerous outbreaks during the past 30 years. PRRSV infection causes reproductive failure in sows and respiratory disease in growing and finishing pigs, leading to huge economic losses for the swine industry. This impact has become even more significant with the recent emergence of highly pathogenic PRRSV strains from China, further exacerbating global food security. Since new PRRSV variants are constantly emerging from outbreaks, current strategies for controlling PRRSV have been largely inadequate, even though our understanding of PRRSV virology, evolution and host immune response has been rapidly expanding. Meanwhile, practical experience has revealed numerous safety and efficacy concerns for currently licensed vaccines, such as shedding of modified live virus (MLV), reversion to virulence, recombination between field strains and MLV and failure to elicit protective immunity against heterogeneous virus. Therefore, an effective vaccine against PRRSV infection is urgently needed. Here, we systematically review recent advances in PRRSV vaccine development. Antigenic variations resulting from PRRSV evolution, identification of neutralizing epitopes for heterogeneous isolates, broad neutralizing antibodies against PRRSV, chimeric virus generated by reverse genetics, and novel PRRSV strains with interferon-inducing phenotype will be discussed in detail. Moreover, techniques that could potentially transform current MLV vaccines into a superior vaccine will receive special emphasis, as will new insights for future PRRSV vaccine development. Ultimately, improved PRRSV vaccines may overcome the disadvantages of current vaccines and minimize the PRRS impact to the swine industry.

Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at: https://github.com/lanagarmire/NMFEM.

Droplet Microarray Based on Superhydrophobic-Superhydrophilic Patterns for Single Cell Analysis

Single-cell analysis provides fundamental information on individual cell response to different environmental cues and is a growing interest in cancer and stem cell research. However, current existing methods are still facing challenges in performing such analysis in a high-throughput manner whilst being cost-effective. Here we established the Droplet Microarray (DMA) as a miniaturized screening platform for high-throughput single-cell analysis. Using the method of limited dilution and varying cell density and seeding time, we optimized the distribution of single cells on the DMA. We established culturing conditions for single cells in individual droplets on DMA obtaining the survival of nearly 100% of single cells and doubling time of single cells comparable with that of cells cultured in bulk cell population using conventional methods. Our results demonstrate that the DMA is a suitable platform for single-cell analysis, which carries a number of advantages compared with existing technologies allowing for treatment, staining and spot-to-spot analysis of single cells over time using conventional analysis methods such as microscopy.

Single Cell Isolation and Analysis

Individual cell heterogeneity within a population can be critical to its peculiar function and fate. Subpopulations studies with mixed mutants and wild types may not be as informative regarding which cell responds to which drugs or clinical treatments. Cell to cell differences in RNA transcripts and protein expression can be key to answering questions in cancer, neurobiology, stem cell biology, immunology, and developmental biology. Conventional cell-based assays mainly analyze the average responses from a population of cells, without regarding individual cell phenotypes. To better understand the variations from cell to cell, scientists need to use single cell analyses to provide more detailed information for therapeutic decision making in precision medicine. In this review, we focus on the recent developments in single cell isolation and analysis, which include technologies, analyses and main applications. Here, we summarize the historical background, limitations, applications, and potential of single cell isolation technologies.

Gene expression variability in mammalian embryonic stem cells using single cell RNA-seq data

BACKGROUND

Gene expression heterogeneity contributes to development as well as disease progression. Due to technological limitations, most studies to date have focused on differences in mean expression across experimental conditions, rather than differences in gene expression variance. The advent of single cell RNA sequencing has now made it feasible to study gene expression heterogeneity and to characterise genes based on their coefficient of variation.

METHODS

We collected single cell gene expression profiles for 32 human and 39 mouse embryonic stem cells and studied correlation between diverse characteristics such as network connectivity and coefficient of variation (CV) across single cells. We further systematically characterised properties unique to High CV genes.

RESULTS

Highly expressed genes tended to have a low CV and were enriched for cell cycle genes. In contrast, High CV genes were co-expressed with other High CV genes, were enriched for bivalent (H3K4me3 and H3K27me3) marked promoters and showed enrichment for response to DNA damage and DNA repair.

CONCLUSIONS

Taken together, this analysis demonstrates the divergent characteristics of genes based on their CV. High CV genes tend to form co-expression clusters and they explain bivalency at least in part.

Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing

Technical advances have enabled the collection of genome and transcriptome data sets with single-cell resolution. However, single-cell characterization of the epigenome has remained challenging. Furthermore, because cells must be physically separated before biochemical processing, conventional single-cell preparatory methods scale linearly. We applied combinatorial cellular indexing to measure chromatin accessibility in thousands of single cells per assay, circumventing the need for compartmentalization of individual cells. We report chromatin accessibility profiles from more than 15,000 single cells and use these data to cluster cells on the basis of chromatin accessibility landscapes. We identify modules of coordinately regulated chromatin accessibility at the level of single cells both between and within cell types, with a scalable method that may accelerate progress toward a human cell atlas.

Cancer: we should not forget the past

Cancer has been in existence longer than human beings, and man has been facing the illness ever since he made his appearance on Earth. Amazingly, the first human cancer gene was cloned only thirty years ago. This, and other extraordinary scientific goals achieved by molecular cancer research in the last 30 years, seems to suggest that definitive answers and solutions to this severe disease have been finally found. This was not the case, as cancer still remains to be defeated. To do so, cancer must be first understood. This review highlights how cancer onset and progression has been tackled from ancient times to present day. Old theories and achievements have provided the pillars of cancer understanding, in laying the basis of 'modern era' cancer research, are discussed. The review highlights the discovery of oncogenes and suppressor tumor genes, underlining the crucial role of these achievements in cancer diagnosis and therapies. Finally, an overview of how the modern technologies have given impetuous to expedite these goals is also considered.

Interrelationship between type three secretion system and metabolism in pathogenic bacteria

Before the advent of molecular biology methods, studies of pathogens were dominated by analyses of their metabolism. Development of molecular biology techniques then enabled the identification and functional characterisation of the fascinating toolbox of virulence factors. Increasing, genomic and proteomic approaches form the basis for a more systemic view on pathogens' functions in the context of infection. Re-emerging interest in the metabolism of pathogens and hosts further expands our view of infections. There is increasing evidence that virulence functions and metabolism of pathogens are extremely intertwined. Type three secretion systems (T3SSs) are major virulence determinants of many Gram-negative pathogens and it is the objective of this review to illustrate the intertwined relationship between T3SSs and the metabolism of the pathogens deploying them.