Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics

Thin Cells of Polymer-Modified Liquid Crystals Described by Voronoi Diagrams

We investigated patterns formed during the polymerization process of bifunctional monomers in a liquid crystal for both large polymer concentrations (polymer-dispersed liquid crystals, PDLC) and small concentrations (polymer-stabilized liquid crystals, PSLC). The resulting experimental patterns are reminiscent of Voronoi diagrams, so a reverse Voronoi algorithm was developed that provides the seed locations of cells, thus allowing a computational reproduction of the experimental patterns. Several metrics were developed to quantify the commonality between the faithful experimental patterns and the idealized and generated ones. This led to descriptions of the experimental patterns with accuracies better than 90% and showed that the curvature or concavity of the cell edges was below 2%. Possible reasons for the discrepancies between the original and generated Voronoi diagrams are discussed. The introduced algorithm and quantification of the patterns could be transferred to many other experimental problems, for example, melting of thin polymer films, ultra-thin metal films, or bio-membranes. The discrepancies between the experimental and ideal Voronoi diagrams are quantified, which may be useful in the quality control of privacy windows, reflective displays, or smart glass.

Comprehensive analyses of brain cell communications based on multiple scRNA-seq and snRNA-seq datasets for revealing novel mechanism in neurodegenerative diseases

AIMS

Complex cellular communications between glial cells and neurons are critical for brain normal function and disorders, and single-cell level RNA-sequencing datasets display more advantages for analyzing cell communications. Therefore, it is necessary to systematically explore brain cell communications when considering factors such as sex and brain region.

METHODS

We extracted a total of 1,039,459 cells derived from 28 brain single-cell RNA-sequencing (scRNA-seq) or single-nucleus RNA-sequencing (snRNA-seq) datasets from the GEO database, including 12 human and 16 mouse datasets. These datasets were further divided into 71 new sub-datasets when considering disease, sex, and region conditions. In the meanwhile, we integrated four methods to evaluate ligand-receptor interaction score among six major brain cell types (microglia, neuron, astrocyte, oligodendrocyte, OPC, and endothelial cell).

RESULTS

For Alzheimer's disease (AD), disease-specific ligand-receptor pairs when compared with normal sub-datasets, such as SEMA4A-NRP1, were identified. Furthermore, we explored the sex- and region-specific cell communications and identified that WNT5A-ROR1 among microglia cells displayed close communications in male, and SPP1-ITGAV displayed close communications in the meninges region from microglia to neurons. Furthermore, based on the AD-specific cell communications, we constructed a model for AD early prediction and confirmed the predictive performance using multiple independent datasets. Finally, we developed an online platform for researchers to explore brain condition-specific cell communications.

CONCLUSION

This research provided a comprehensive study to explore brain cell communications, which could reveal novel biological mechanisms involved in normal brain function and neurodegenerative diseases such as AD.

Novel Network Method Major Minor Variation Clustering Enables Identification of Poliovirus Clusters with High-Resolution Linkages

The Global Polio Eradication Initiative uses an outbreak response protocol that defines type 2 Sabin or Sabin-like virus as those with 0-5 nucleotides diverging from their parental strain in the complete VP1 genomic region. Sabin or Sabin-like viruses share highly similar genome sequences, regardless of their origin. Thus, it is challenging to distinguish viruses at a higher resolution to detect polio clusters or trace sources for local transmissions of viruses at an early stage. To identify type 2 Sabin or Sabin-like sources and improve our ability to map viral sources to campaigns during the polio endgame, we investigated the feasibility of a new method for genetic sequence analysis. We named the method Major Minor Variation Clustering (MMVC), which uses a network model to simultaneously incorporate sequence similarity in major and minor variants in addition to onset dates to detect fine-scale polio clusters. Each identified cluster represents a collection of sequences that are highly similar in both major and minor variants, enabling the discovery of new links between viruses. By applying the method to a published data set collected in Nigeria during 2009-2012, we found that clusters identified using this method have several improvements over clusters derived from a phylogenetic tree approach. Integrative data analysis reveals that sequences in the same cluster have greater genomic similarities and better agreement with onset dates. As a complement to current phylogenetic tree approaches, MMVC has the potential to improve epidemiological surveillance and investigation precision to guide polio eradication.

Neurons and Astrocytes Elicit Brain Region Specific Transcriptional Responses to Prion Disease in the Murine CA1 and Thalamus

Progressive dysfunction and loss of neurons ultimately culminates in the symptoms and eventual fatality of prion disease, yet the pathways and mechanisms that lead to neuronal degeneration remain elusive. Here, we used RNAseq to profile transcriptional changes in microdissected CA1 and thalamus brain tissues from prion infected mice. Numerous transcripts were altered during clinical disease, whereas very few transcripts were reliably altered at pre-clinical time points. Prion altered transcripts were assigned to broadly defined brain cell types and we noted a strong transcriptional signature that was affiliated with reactive microglia and astrocytes. While very few neuronal transcripts were common between the CA1 and thalamus, we described transcriptional changes in both regions that were related to synaptic dysfunction. Using transcriptional profiling to compare how different neuronal populations respond during prion disease may help decipher mechanisms that lead to neuronal demise and should be investigated with greater detail.

The Tsallis generalized entropy enhances the interpretation of transcriptomics datasets

Background

Identifying differentially expressed genes between experimental conditions is still the gold-standard approach to interpret transcriptomic profiles. Alternative approaches based on diversity measures have been proposed to complement the interpretation of such datasets but are only used marginally.

Methods

Here, we reinvestigated diversity measures, which are commonly used in ecology, to characterize mice pregnancy microenvironments based on a public transcriptome dataset. Mainly, we evaluated the Tsallis entropy function to explore the potential of a collection of diversity measures for capturing relevant molecular event information.

Results

We demonstrate that the Tsallis entropy function provides additional information compared to the traditional diversity indices, such as the Shannon and Simpson indices. Depending on the relative importance given to the most abundant transcripts based on the Tsallis entropy function parameter, our approach allows appreciating the impact of biological stimulus on the inter-individual variability of groups of samples. Moreover, we propose a strategy for reducing the complexity of transcriptome datasets using a maximation of the beta diversity.

Conclusions

We highlight that a diversity-based analysis is suitable for capturing complex molecular events occurring during physiological events. Therefore, we recommend their use through the Tsallis entropy function to analyze transcriptomics data in addition to differential expression analyses.

Integrating single-cell transcriptomics and microcircuit computer modeling

Biophysically realistic computer modeling of neuronal microcircuitry has served as a testing ground for hypotheses related to the structure and function of different brain microcircuits. Recent advances in single-cell transcriptomics provide snapshots of a neuron's molecular state and have demonstrated that cell-specific genetic markers engineer the electrophysiological properties of a neuron. Integrating these molecular details with biophysical modeling can allow unprecedented mechanistic insights. In this opinion review, we consider systems biology-based strategies involving statistical deconvolution and gene ontology to integrate the two approaches. We foresee that this integration will infer the nonlinear interactions between the transcriptomically detailed neurons in different brain states. For an initial assessment of these integrative strategies, we recommend testing them on a penetrant phenotype such as epilepsy or a basic organism model such as Caenorhabditis elegans.

Goals and approaches for each processing step for single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data.

scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimer's disease), which is as far as we know the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq data sets from the human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 73 data sets from 10 brain regions, including control atlas construction, cell-type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons.