A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain.

The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.

Bioinformatics approaches to single-cell analysis in developmental biology.

Individual cells within the same population show various degrees of heterogeneity, which may be better handled with single-cell analysis to address biological and clinical questions. Single-cell analysis is especially important in developmental biology as subtle spatial and temporal differences in cells have significant associations with cell fate decisions during differentiation and with the description of a particular state of a cell exhibiting an aberrant phenotype. Biotechnological advances, especially in the area of microfluidics, have led to a robust, massively parallel and multi-dimensional capturing, sorting, and lysis of single-cells and amplification of related macromolecules, which have enabled the use of imaging and omics techniques on single cells. There have been improvements in computational single-cell image analysis in developmental biology regarding feature extraction, segmentation, image enhancement and machine learning, handling limitations of optical resolution to gain new perspectives from the raw microscopy images. Omics approaches, such as transcriptomics, genomics and epigenomics, targeting gene and small RNA expression, single nucleotide and structural variations and methylation and histone modifications, rely heavily on high-throughput sequencing technologies. Although there are well-established bioinformatics methods for analysis of sequence data, there are limited bioinformatics approaches which address experimental design, sample size considerations, amplification bias, normalization, differential expression, coverage, clustering and classification issues, specifically applied at the single-cell level. In this review, we summarize biological and technological advancements, discuss challenges faced in the aforementioned data acquisition and analysis issues and present future prospects for application of single-cell analyses to developmental biology.

Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets.

BACKGROUND: To analyze the large volume of data generated by single-cell technologies and to identify cellular correlates of particular clinical or experimental outcomes, differential abundance analyses are often applied. These algorithms identify subgroups of cells whose abundances change significantly in response to disease progression, or to an experimental perturbation. Despite the effectiveness of differential abundance analyses in identifying critical cell-states, there is currently no systematic benchmarking study to compare their applicability, usefulness, and accuracy in practice across single-cell modalities. RESULTS: Here, we perform a comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-art differential abundance testing methods. We benchmarked six single-cell testing methods on several practical tasks, using both synthetic and real single-cell datasets. The tasks evaluated include effectiveness in identifying true differentially abundant subpopulations, accuracy in the adequate handling of batch effects, runtime efficiency, and hyperparameter usability and robustness. Based on various evaluation results, this paper gives dataset-specific suggestions for the practical use of differential abundance testing approaches. CONCLUSIONS: Based on our benchmarking study, we provide a set of recommendations for the optimal usage of single-cell DA testing methods in practice, particularly with respect to factors such as the presence of technical noise (for example batch effects), dataset size, and hyperparameter sensitivity.

Cisplatin effect on digital cytomorphometric and bioinformatic tumor cell characteristics in rat ovarian cancer model-a preliminary study.

BACKGROUND: Ovarian cancer is one of the most common diseases of the female reproductive system. The aim of this study was the investigation of the antitumor cisplatin effect on ascitic fluid tumor cells in the ovarian cancer experimental model by digital cytomorphometry and cell bioinformatic analysis. METHODS: Female Wistar rats were inoculated by ovarian cancer strain. After ovarian cancer transplantation rats were divided into two groups: control group-without cisplatin treatment, the experimental group-under cisplatin treatment. The ascitic fluid was taken on the 9-th day after tumor cell inoculation. Digital cytomorphometric and cytobioinformatic analysis were used to study ascitic fluid cancer cell morphofunctional changes under cisplatin treatment. RESULTS: Digital cytomorphometric characteristics of rat ovarian cancer ascitic cells were obtained. Tumor cells were classified into four groups according to their geometric size: small, medium, large, "gigantic". The algorithm and the computer program based on tumor cell morphometric characteristics were created to calculate such cell bioinformatic characteristic as information redundancy coefficient R. Three parameters were determined as the criteria of cisplatin effect on cancer cells: cell number, nuclear/cytoplasmic ratio, R-value. CONCLUSIONS: Data obtained suggest that cisplatin reduces the total cell number, the nuclear/cytoplasmic ratio and R-value thus indicates a decrease in cellular resistance and adaptive potential. The digital cytomorphometry and bioinformatics could be recommended as a testing system in the experimental or clinical study.

RNA-Seq analysis reveals pluripotency-associated genes and their interaction networks in human embryonic stem cells.

Insight into the key genes of pluripotency in human and their interrelationships is necessary for understanding the underlying mechanism of pluripotency and hence their successful application in regenerative medicine. The recent advances in transcriptomics technologies have created new opportunities to decipher the genes involved in pluripotency, genetic network that governs the unique properties of embryonic stem cells and lineage differentiation mechanisms in a deeper scale. There are a large number of experimental studies on human embryonic stem cells (hESCs) being routinely conducted for unfolding the underlying biology of embryogenesis and their clinical prospects. However, the outcome of these studies often lacks consensus due to differences in samples, experimental techniques and/or analysis protocols. A universal stemness gene list is still lacking. Thus, we aim to identify the pluripotency-associated genes and their interaction network. In this quest, we compared transcriptomic profiles of pluripotent and non-pluripotent samples from diverse cell lines/types generated through RNA-sequencing (RNA-seq). We used a uniform pipeline for the analysis of raw RNA-seq data in order to reduce the amount of variation. Our analysis revealed a consensus set of 498 pluripotency-associated genes and 432 genes as potential pluripotent cell differentiation markers. Furthermore, we predicted 32 genes as "pluripotency critical genes". These pluripotency critical genes formed a tightly bound co-expression network with small-world architecture. Gene ontology (GO) and pathway enrichment analysis, StemChecker and literature survey confirmed the involvement of the genes in the induction and maintenance of pluripotency, though more experimental studies are required for understanding their molecular mechanisms in human.

Transcriptome Profiling of Single Mouse Oocytes.

Single-cell RNA-sequencing (scRNAseq) enables the detection and quantification of mature RNAs in an individual cell. Assessing single cell transcriptomes can circumvent the limited amount of starting material obtained in oocytes or embryos, in particular when working with mutant mice. Here we outline our scRNAseq protocol to study mouse oocyte transcriptomes, derived from Tang et al., Nat Methods 6(5):377-382, 2009 . The method describes the different steps from single cell isolation and cDNA amplification to high-throughput sequencing. The bioinformatics pipeline used to analyze and compare genome-wide gene expression between individual oocytes is then described.