A variety of single-cell RNA preparation procedures have been described. So far, protocols require fresh material, which hinders complex study designs. We describe a sample preservation method that maintains transcripts in viable single cells, allowing one to disconnect time and place of sampling from subsequent processing steps. We sequence single-cell transcriptomes from >1000 fresh and cryopreserved cells using 3'-end and full-length RNA preparation methods. Our results confirm that the conservation process did not alter transcriptional profiles. This substantially broadens the scope of applications in single-cell transcriptomics and could lead to a paradigm shift in future study designs.

Planarians can undergo dramatic changes in body size and regenerate their entire body plan from small pieces after cutting. This remarkable morphological plasticity has made them an excellent model in which to analyze phenomena such as morphogenesis, restoration of pattern and polarity, control of tissue proportions and tissue homeostasis. They have a unique population of pluripotent stem cells in the adult that can give rise to all differentiated cell types, including the germ cells. These cellular characteristics provide an excellent opportunity to study the mechanisms involved in the maintenance and differentiation of cell populations in intact and regenerating animals. Until recently, the planarian model system lacked opportunities for genetic analysis; however, this handicap was overcome in the last decade through the development of new molecular methods which have been successfully applied to planarians. These techniques have allowed analysis of the temporal and spatial expression of genes, as well as interference with gene function, generating the first phenotypes by loss or gain of function. Finally, the sequencing of the planarian genome has provided the essential tools for an in-depth analysis of the genomic regulation of this model system. In this review, we provide an overview of planarians as a model system for research into development and regeneration and describe new lines of investigation in this area.

The development of a complex multicellular organism requires a careful coordination of growth, cell division, cell differentiation and cell death. All these processes must be under intricate and coordinated control, as they have to be integrated across all tissues. Freshwater planarians are especially plastic, in that they constantly replace somatic tissues from a pool of adult somatic stem cells and continuously undergo growth and degrowth as adult animals in response to nutrient availability. During these processes they appear to maintain perfect scale of tissues and organs. These life history traits make them an ideal model system to study growth and degrowth. We have studied the unique planarian process of degrowth. When food is not available, planarians are able to degrow to a minimum size, without any signs of adverse physiological outcomes. For example they maintain full regenerative capacity. Our current knowledge of how this is regulated at the molecular and cellular level is very limited. Planarian degrowth has been reported to result from a decrease in cell number rather than a decrease in cell size. Thus one obvious explanation for degrowth would be a decrease in stem cell proliferation. However evidence in the literature suggests this is not the case. We show that planarians maintain normal basal mitotic rates during degrowth but that the number of stem cell progeny decreases during starvation and degrowth. These observations are reversed upon feeding, indicating that they are dependent on nutritional status. An increase in cell death is also observed during degrowth, which is not rapidly reversed upon feeding. We conclude that degrowth is a result of cell death decreasing cell numbers and that the dynamics of neoblast self-renewal and differentiation adapt to nutrient conditions to allow maintenance of the neoblast population during the period of starvation.

Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.

Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.