Breast cancer tumors display different cellular phenotypes. A growing body of evidence points toward a population of cancer stem cells (CSCs) that is important for metastasis and treatment resistance, although the characteristics of these cells are incomplete. We used mammosphere formation assay and label-retention assay as functional cellular approaches to enrich for cells with different degree of CSC properties in the breast cancer cell line MDA-MB-231 and performed single-cell RNA sequencing. We clustered the cells based on their gene expression profiles and identified three subpopulations, including a CSC-like population. The cell clustering into these subpopulations overlapped with the cellular enrichment approach applied. To molecularly define these groups, we identified genes differentially expressed between the three subpopulations which could be matched to enriched gene sets. We also investigated the transition process from CSC-like cells into more differentiated cell states. In the CSC population we found 14 significantly upregulated genes. Some of these potential breast CSC markers are associated to reported stem cell properties and clinical survival data, but further experimental validation is needed to confirm their cellular functions. Detailed characterization of CSCs improve our understanding of mechanisms for tumor progression and contribute to the identification of new treatment targets.

Cell migration is a fundamental biological process of key importance in health and disease. Advances in imaging techniques have paved the way to monitor cell motility. An ever-growing collection of computational tools to track cells has improved our ability to analyze moving cells. One renowned goal in the field is to provide tools that track cell movement as comprehensively and automatically as possible. However, fully automated tracking over long intervals of time is challenged by dividing cells, thus calling for a combination of automated and supervised tracking. Furthermore, after the emergence of various experimental tools to monitor cell-cycle phases, it is of relevance to integrate the monitoring of cell-cycle phases and motility. We developed CellMAPtracer, a multiplatform tracking system that achieves that goal. It can be operated as a conventional, automated tracking tool of single cells in numerous imaging applications. However, CellMAPtracer also allows adjusting tracked cells in a semiautomated supervised fashion, thereby improving the accuracy and facilitating the long-term tracking of migratory and dividing cells. CellMAPtracer is available with a user-friendly graphical interface and does not require any coding or programming skills. CellMAPtracer is compatible with two- and three-color fluorescent ubiquitination-based cell-cycle indicator (FUCCI) systems and allows the user to accurately monitor various migration parameters throughout the cell cycle, thus having great potential to facilitate new discoveries in cell biology.

Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.