Investigating evolutionary perspective of carcinogenesis with single-cell transcriptome analysis

Revealing the molecular mechanism of central nervous system leukemia with single-cell technology

Central nervous system leukemia (CNSL) is a severe complication of acute leukemia, with serious consequences for life quality and expectancy. The molecular mechanism of CNSL is unclear at present. Thus, determining appropriate prevention and therapeutic strategies for CNSL remain challenging. Currently, inferences regarding gene functions are based on the measurement of average gene expression in a bulk lysate. However, leukemia cells are a heterogeneous population in which the expression of critical genes may be masked by many unrelated genes. Single-cell sequencing may therefore be the best way to explore the development of CNSL in the bone marrow and peripheral blood at diagnosis and subsequent time points, in order to detect potential targets and prevent the development of CNSL. In this review, we first discuss the possible mechanism of CNSL, then describe the heterogeneity of leukemia cells. Finally, we focus on the role of single-cell technology in preventing and treating CNSL.

Genetic mutations associated with metastatic clear cell renal cell carcinoma

Metastasis is the major cause of death among cancer patients, yet early detection and intervention of metastasis could significantly improve their clinical outcomes. We have sequenced and analyzed RNA (Expression) and DNA (Mutations) from the primary tumor (PT), tumor extension (TE) and lymphatic metastatic (LM) sites of patients with clear cell renal cell carcinoma (CCRCC) before treatment. Here, we report a three-nucleotide deletion near the C-region of Plk5 that is specifically associated with the lymphatic metastasis. This mutation is un-detectable in the PT, becomes detectable in the TE and dominates the LM tissue. So while only a few primary cancer cells carry this mutation, the majority of metastatic cells have this mutation. The increasing frequency of this mutation in metastatic tissue suggests that this Plk5 deletion could be used as an early indicator of CCRCC metastasis, and be identified by low cost PCR assay. A large scale clinical trial could reveal whether a simple PCR assay for this mutation at the time of nephrectomy could identify and stratify high-risk CCRCC patients for treatments.

Single-cell genomic analysis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) incidence or rates have increased dramatically recently with little improvement in patient outcomes. There is an unmet need in HNSCC to develop reliable molecular markers capable of evaluating patient risks and advising treatments. This review focuses on recent developments in single-cell molecular analysis of cancer, and its applications for HNSCC diagnosis and treatments. For proof of concept, we examined gene expression levels of 62 patients with HNSCC, and correlate the gene expression profiles to single-cell gene expression profiles obtained from a pilot single-cell study of CCR5-positive breast carcinoma cells. The single-cell molecular analyses complemented the lysate data and reveals heterogeneity of oncogenesis pathways with the cancer cell population. Our single-cell molecular analysis indicated that molecular heterogeneity exists in HNSCC and should be addressed in treatment strategy of HNSCC. Single-cell molecular technology can have significant impact on diagnosis, therapeutic decision making, and prognosis of HNSCC.

Cells Respond to Distinct Nanoparticle Properties with Multiple Strategies As Revealed by Single-Cell RNA-Seq

The impact of distinct nanoparticle (NP) properties on cellular response and ultimately human health is unclear. This gap is partially due to experimental difficulties in achieving uniform NP loads in the studied cells, creating heterogeneous populations with some cells "overloaded" while other cells are loaded with few or no NPs. Yet gene expression studies have been conducted in the population as a whole, identifying generic responses, while missing unique responses due to signal averaging across many cells, each carrying different loads. Here, we applied single-cell RNA-Seq to alveolar epithelial cells carrying defined loads of aminated or carboxylated quantum dots (QDs), showing higher or lower toxicity, respectively. Interestingly, cells carrying lower loads responded with multiple strategies, mostly with up-regulated processes, which were nonetheless coherent and unique to each QD type. In contrast, cells carrying higher loads responded more uniformly, with mostly down-regulated processes that were shared across QD types. Strategies unique to aminated QDs showed strong up-regulation of stress responses, coupled in some cases with regulation of cell cycle, protein synthesis, and organelle activities. In contrast, strategies unique to carboxylated QDs showed up-regulation of DNA repair and RNA activities and decreased regulation of cell division, coupled in some cases with up-regulation of stress responses and ATP-related functions. Together, our studies suggest scenarios where higher NP loads lock cells into uniform responses, mostly shutdown of cellular processes, whereas lower loads allow for unique responses to each NP type that are more diversified proactive defenses or repairs of the NP insults.

Single-cell Transcriptome Study as Big Data

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.

Single cell analysis of cancer genomes

Genomic studies have provided key insights into how cancers develop, evolve, metastasize and respond to treatment. Cancers result from an interplay between mutation, selection and clonal expansions. In solid tumours, this Darwinian competition between subclones is also influenced by topological factors. Recent advances have made it possible to study cancers at the single cell level. These methods represent important tools to dissect cancer evolution and provide the potential to considerably change both cancer research and clinical practice. Here we discuss state-of-the-art methods for the isolation of a single cell, whole-genome and whole-transcriptome amplification of the cell's nucleic acids, as well as microarray and massively parallel sequencing analysis of such amplification products. We discuss the strengths and the limitations of the techniques, and explore single-cell methodologies for future cancer research, as well as diagnosis and treatment of the disease.