Artificial Intelligence-Driven Morphology-Based Enrichment of Malignant Cells from Body Fluid.

Cell morphology is a fundamental feature used to evaluate patient specimens in pathologic analysis. However, traditional cytopathology analysis of patient effusion samples is limited by low tumor cell abundance coupled with the high background of nonmalignant cells, restricting the ability of downstream molecular and functional analyses to identify actionable therapeutic targets. We applied the Deepcell platform that combines microfluidic sorting, brightfield imaging, and real-time deep learning interpretations based on multidimensional morphology to enrich carcinoma cells from malignant effusions without cell staining or labels. Carcinoma cell enrichment was validated with whole genome sequencing and targeted mutation analysis, which showed a higher sensitivity for detection of tumor fractions and critical somatic variant mutations that were initially at low levels or undetectable in presort patient samples. Our study demonstrates the feasibility and added value of supplementing traditional morphology-based cytology with deep learning, multidimensional morphology analysis, and microfluidic sorting.

Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity.

Several cell populations have been reported to possess intestinal stem cell (ISC) activity during homeostasis and injury-induced regeneration. Here, we explored inter-relationships between putative mouse ISC populations by comparative RNA-sequencing (RNA-seq). The transcriptomes of multiple cycling ISC populations closely resembled Lgr5+ ISCs, the most well-defined ISC pool, but Bmi1-GFP+ cells were distinct and enriched for enteroendocrine (EE) markers, including Prox1. Prox1-GFP+ cells exhibited sustained clonogenic growth in vitro, and lineage-tracing of Prox1+ cells revealed long-lived clones during homeostasis and after radiation-induced injury in vivo. Single-cell mRNA-seq revealed two subsets of Prox1-GFP+ cells, one of which resembled mature EE cells while the other displayed low-level EE gene expression but co-expressed tuft cell markers, Lgr5 and Ascl2, reminiscent of label-retaining secretory progenitors. Our data suggest that the EE lineage, including mature EE cells, comprises a reservoir of homeostatic and injury-inducible ISCs, extending our understanding of cellular plasticity and stemness.

Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.

Neural stem cells (NSCs) in the adult mammalian brain serve as a reservoir for the generation of new neurons, oligodendrocytes, and astrocytes. Here, we use single-cell RNA sequencing to characterize adult NSC populations and examine the molecular identities and heterogeneity of in vivo NSC populations. We find that cells in the NSC lineage exist on a continuum through the processes of activation and differentiation. Interestingly, rare intermediate states with distinct molecular profiles can be identified and experimentally validated, and our analysis identifies putative surface markers and key intracellular regulators for these subpopulations of NSCs. Finally, using the power of single-cell profiling, we conduct a meta-analysis to compare in vivo NSCs and in vitro cultures, distinct fluorescence-activated cell sorting strategies, and different neurogenic niches. These data provide a resource for the field and contribute to an integrative understanding of the adult NSC lineage.

An artificial niche preserves the quiescence of muscle stem cells and enhances their therapeutic efficacy.

A promising therapeutic strategy for diverse genetic disorders involves transplantation of autologous stem cells that have been genetically corrected ex vivo. A major challenge in such approaches is a loss of stem cell potency during culture. Here we describe an artificial niche for maintaining muscle stem cells (MuSCs) in vitro in a potent, quiescent state. Using a machine learning method, we identified a molecular signature of quiescence and used it to screen for factors that could maintain mouse MuSC quiescence, thus defining a quiescence medium (QM). We also engineered muscle fibers that mimic the native myofiber of the MuSC niche. Mouse MuSCs maintained in QM on engineered fibers showed enhanced potential for engraftment, tissue regeneration and self-renewal after transplantation in mice. An artificial niche adapted to human cells similarly extended the quiescence of human MuSCs in vitro and enhanced their potency in vivo. Our approach for maintaining quiescence may be applicable to stem cells isolated from other tissues.

The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data.

Rapid development of next generation sequencing technology has enabled the identification of genomic alterations from short sequencing reads. There are a number of software pipelines available for calling single nucleotide variants from genomic DNA but, no comprehensive pipelines to identify, annotate and prioritize expressed SNVs (eSNVs) from non-directional paired-end RNA-Seq data. We have developed the eSNV-Detect, a novel computational system, which utilizes data from multiple aligners to call, even at low read depths, and rank variants from RNA-Seq. Multi-platform comparisons with the eSNV-Detect variant candidates were performed. The method was first applied to RNA-Seq from a lymphoblastoid cell-line, achieving 99.7% precision and 91.0% sensitivity in the expressed SNPs for the matching HumanOmni2.5 BeadChip data. Comparison of RNA-Seq eSNV candidates from 25 ER+ breast tumors from The Cancer Genome Atlas (TCGA) project with whole exome coding data showed 90.6-96.8% precision and 91.6-95.7% sensitivity. Contrasting single-cell mRNA-Seq variants with matching traditional multicellular RNA-Seq data for the MD-MB231 breast cancer cell-line delineated variant heterogeneity among the single-cells. Further, Sanger sequencing validation was performed for an ER+ breast tumor with paired normal adjacent tissue validating 29 out of 31 candidate eSNVs. The source code and user manuals of the eSNV-Detect pipeline for Sun Grid Engine and virtual machine are available at http://bioinformaticstools.mayo.edu/research/esnv-detect/.