Recent advances and current issues in single-cell sequencing of tumors

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

Plant biotechnology research with single-cell transcriptome: recent advancements and prospects

KEY MESSAGE

Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research. Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.

Liver in infections: a single-cell and spatial transcriptomics perspective

The liver is an immune organ that plays a vital role in the detection, capture, and clearance of pathogens and foreign antigens that invade the human body. During acute and chronic infections, the liver transforms from a tolerant to an active immune state. The defence mechanism of the liver mainly depends on a complicated network of intrahepatic and translocated immune cells and non-immune cells. Therefore, a comprehensive liver cell atlas in both healthy and diseased states is needed for new therapeutic target development and disease intervention improvement. With the development of high-throughput single-cell technology, we can now decipher heterogeneity, differentiation, and intercellular communication at the single-cell level in sophisticated organs and complicated diseases. In this concise review, we aimed to summarise the advancement of emerging high-throughput single-cell technologies and re-define our understanding of liver function towards infections, including hepatitis B virus, hepatitis C virus, Plasmodium, schistosomiasis, endotoxemia, and corona virus disease 2019 (COVID-19). We also unravel previously unknown pathogenic pathways and disease mechanisms for the development of new therapeutic targets. As high-throughput single-cell technologies mature, their integration into spatial transcriptomics, multiomics, and clinical data analysis will aid in patient stratification and in developing effective treatment plans for patients with or without liver injury due to infectious diseases.

Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives

Plants contain a large number of cell types and exhibit complex regulatory mechanisms. Studies at the single-cell level have gradually become more common in plant science. Single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics techniques have been combined to analyze plant development. These techniques have been used to study the transcriptomes and metabolomes of plant tissues at the single-cell level, enabling the systematic investigation of gene expression and metabolism in specific tissues and cell types during defined developmental stages. In this review, we present an overview of significant breakthroughs in spatial multi-omics in plants, and we discuss how these approaches may soon play essential roles in plant research.

Drug repurposing in cancer neuroscience: From the viewpoint of the autophagy-mediated innervated niche

Based on the bidirectional interactions between neurology and cancer science, the burgeoning field “cancer neuroscience” has been proposed. An important node in the communications between nerves and cancer is the innervated niche, which has physical contact with the cancer parenchyma or nerve located in the proximity of the tumor. In the innervated niche, autophagy has recently been reported to be a double-edged sword that plays a significant role in maintaining homeostasis. Therefore, regulating the innervated niche by targeting the autophagy pathway may represent a novel therapeutic strategy for cancer treatment. Drug repurposing has received considerable attention for its advantages in cost-effectiveness and safety. The utilization of existing drugs that potentially regulate the innervated niche via the autophagy pathway is therefore a promising pharmacological approach for clinical practice and treatment selection in cancer neuroscience. Herein, we present the cancer neuroscience landscape with an emphasis on the crosstalk between the innervated niche and autophagy, while also summarizing the underlying mechanisms of candidate drugs in modulating the autophagy pathway. This review provides a strong rationale for drug repurposing in cancer treatment from the viewpoint of the autophagy-mediated innervated niche.

Microfluidics applications for high-throughput single cell sequencing

The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.

An Optimised Protocol Harnessing Laser Capture Microdissection for Transcriptomic Analysis on Matched Primary and Metastatic Colorectal Tumours

Generation of large amounts of genomic data is now feasible and cost-effective with improvements in next generation sequencing (NGS) technology. Ribonucleic acid sequencing (RNA-Seq) is becoming the preferred method for comprehensively characterising global transcriptome activity. Unique to cytoreductive surgery (CRS), multiple spatially discrete tumour specimens could be systematically harvested for genomic analysis. To facilitate such downstream analyses, laser capture microdissection (LCM) could be utilized to obtain pure cell populations. The aim of this protocol study was to develop a methodology to obtain high-quality expression data from matched primary tumours and metastases by utilizing LCM to isolate pure cellular populations. We demonstrate an optimized LCM protocol which reproducibly delivered intact RNA used for RNA sequencing and quantitative polymerase chain reaction (qPCR). After pathologic annotation of normal epithelial, tumour and stromal components, LCM coupled with cDNA library generation provided for successful RNA sequencing. To illustrate our framework's potential to identify targets that would otherwise be missed with conventional bulk tumour sequencing, we performed qPCR and immunohistochemical technical validation to show that the genes identified were truly expressed only in certain sub-components. This study suggests that the combination of matched tissue specimens with tissue microdissection and NGS provides a viable platform to unmask hidden biomarkers and provides insight into tumour biology at a higher resolution.

Predicting clone genotypes from tumor bulk sequencing of multiple samples

Motivation

Analyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known.

Results

We have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by PhyloWGS, and BayClone2, Canopy and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets. Overall, the deconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference.

Availability and implementation

CloneFinder is implemented in Python and is available from https://github.com/gstecher/CloneFinderAPI.

Supplementary information

Supplementary data are available at Bioinformatics online.

Rho/Rho-associated protein kinase signaling pathway-mediated downregulation of runt-related transcription factor 2 expression promotes the differentiation of dental pulp stem cells into odontoblasts

The present study investigated the role of runt-related transcription factor 2 (Runx2) in regulating the differentiation of human dental pulp stem cells (hDPSCs) into odontoblasts under the mediation of the Rho/Rho-associated protein kinase (ROCK) signaling pathway. hDPSCs and human bone marrow mesenchymal stem cells (hBMSCs) were mineralized to induce differentiation. The expression levels of odontoblast- and osteoblast-specific proteins, dentin sialophosphoprotein (DSPP), osteocalcin (OCN) and Runx2, were measured using western blot analysis. The hDPSCs were treated with Rho/ROCK signaling pathway inhibitor, C3 exoenzyme, and mineralized prior to determining the protein expression levels of RhoA, ROCK, Runx2, OCN, DSPP, and mRNA expression levels of early mineralization genes, including alkaline phosphatase, collagen type I, Msh homeobox 2 and distal-less homeobox 2, and late mineralization genes, including DSPP, dentin matrix protein-1 (DMP-1), bone sialoprotein (BSP) and OCN. Flow cytometry data indicated that 95% of the isolated hDPSCs were positive for mesenchymal stem cell markers, including cluster of differentiation (CD)29, CD90 or CD105, and vascular endothelial cell marker, CD146, whereas <5% of the hDPSCs were positive for hematopoietic stem cell markers, CD34 and CD45. The expression levels of DSPP in hDPSCs and OCN in hBMSCs were significantly upregulated with increased time in mineralization medium (P<0.01), which suggested that hDPSCs and hBMSCs were differentiated into odontoblasts and osteoblasts, respectively. During the osteogenic process, Runx2 protein was highly expressed in mesenchymal stem cells following stimulation with mineralization medium compared with cells that received no stimulation. During odontoblast differentiation in hDPSCs, Runx2 protein was highly expressed in the early stage; however, the expression declined in the late stage. Furthermore, treatment with C3 exoenzyme significantly downregulated the expression of RhoA, ROCK and Runx2 compared with the control in hDPSCs (P<0.01). Additionally, in mineralization solution, C3 exoenzyme also significantly downregulated the expression of Runx2 (P<0.01); however, the Rho/ROCK signaling pathway inhibitor did not significantly impact the expression of early mineralization genes. By contrast, C3 exoenzyme significantly upregulated the expression of DSPP and DMP-1, and downregulated the expression of BSP and OCN (P<0.01). The present findings suggested that odontoblast differentiation in hDPSCs may be regulated by Rho/ROCK signaling pathway-mediated downregulation of Runx2.

Development of locus specific sub-clone separation by fluorescence in situ hybridization in suspension in chronic lymphocytic leukemia

Intra-tumor genetic heterogeneity is a hallmark of cancer. The ability to monitor and analyze these sub-clonal cell populations can be considered key to successful treatment, particularly in the modern era of targeted therapies. Although advances in sequencing technologies have significantly improved our ability to analyze the mutational landscape of tumors, this utility is reduced when considering small, but clinically significant sub-clones, that is, those representing <10% of the tumor burden. We have developed a high-throughput method that utilizes a 17-probe labeled bacterial artificial chromosome contig to quantify sub-clonal populations of cells based on deletion of a single locus. Chronic lymphocytic leukemia (CLL) cells harboring deletion of the short arm of chromosome 17 (del17p), an important prognostic marker for CLL were used to demonstrate the technique. Sub-clones of del17p cells were quantified and isolated from heterogeneous CLL populations using fluorescence in situ hybridization in suspension (FISH-IS) and the locus specific probe set. Using the combination of FISH-IS with the locus-specific probe set enables automated analysis of tens of thousands of cells, accurately quantifying and isolating cells carrying a del17p. Based on the fluorescence intensity of 17p probes, 17p (TP53) deleted cells were identified and sorted using flow cytometric techniques, and enrichment was demonstrated using single nucleotide polymorphism analysis. The ability to separate sub-clones of cells based on genetic heterogeneity, independent of the clone size, highlights the potential application of this method not only in the diagnostic and prognostic setting, but also as an unbiased approach to enable further detailed genetic analysis of the sub-clone with deep sequencing approaches. © 2017 International Society for Advancement of Cytometry.

High-dimension single-cell analysis applied to cancer

High-dimension single-cell technology is transforming our ability to study and understand cancer. Numerous studies and reviews have reported advances in technology development. The biological insights gleaned from single-cell technology about cancer biology are less reviewed. Here we focus on research studies that illustrate novel aspects of cancer biology that bulk analysis could not achieve, and discuss the fresh insights gained from the application of single-cell technology across basic and clinical cancer studies.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification

BACKGROUND

Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.

RESULTS

This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis.

CONCLUSIONS

Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling

Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models ("bottom-up" research) is highlighted. The review concludes that computational modelling is an essential part of cancer research and is vital to understanding network formation and molecular behaviours that are associated with it. Its role is increasingly essential because it is unravelling the huge complexity of cancer induction otherwise unattainable by any other approach.

Antiproliferative activity and apoptosis induction by 3',4'-dibenzyloxyflavonol on human leukemia cells

In this study, we investigated the effects of synthetic 3',4'-dibenzyloxyflavonol on viabilities of eight human tumor cells. It was cytotoxic against leukemia cells (HL-60, U-937, MOLT-3, K-562, NALM-6, Raji), with significant effects against P-glycoprotein-overexpressing K-562/ADR and Bcl-2-overexpressing U-937/Bcl-2 cells, but had no significant cytotoxic effects against quiescent or proliferating human peripheral blood mononuclear cells. The IC₅₀ value for the leukemia HL-60 cells was 0.8 ± 0.1 μM. This indicates a 60-fold greater toxicity than the naturally occurring flavonol quercetin. Synthetic 3',4'-dibenzyloxyflavonol induced S phase cell cycle arrest and was a potent apoptotic inducer in human leukemia cells. Cell death was (i) mediated by the activation and the cleavage of initiator and executioner caspases; (ii) prevented by the pan-caspase inhibitor z-VAD-fmk; (iii) associated with the release of cytochrome c and with the phosphorylation of members of the mitogen activated protein kinases including p38^MAPK, JNK/SAPK and ERK, and (iv) independent of the generation of reactive oxygen species. The synthetic 3',4'-dibenzyloxyflavonol is a potent cytotoxic compound against several human leukemia cells and might be useful in the development of new strategies in the fight against cancer.

Detection of single cell heterogeneity in cancer

Single cell heterogeneity has already been highlighted in cancer classification, diagnosis, and treatment. Recent advanced technologies have gained more ability to reveal the heterogeneity on single cell level. In this review, we listed various detection targets applied in single cell study, including tumor tissue cells, circulating tumor cells (CTCs), disseminated tumor cells (DTCs), circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), and cancer stem cells (CSCs). We further discussed and compared detection methods using these detection targets in different fields to reveal single cell heterogeneity in cancer. We focused not only on the methods that have already been established and validated, but also on newly developed methods. In morphology and phenotype, the methods mainly included cell imaging and immune-staining. In genomics and proteomics, the main methods were single cell sequencing and single cell western blotting. Collectively, from using these methods, we can have a better understanding of the single cell variation, as well as what kind of variation it is and how the variation works. Our observations imply that study on single cell heterogeneity in cancer is an important step to precision medicine. The development of technologies in detection of single cell heterogeneity will be sure to improve the diagnosis and treatment in cancer.

Single-cell approaches for molecular classification of endocrine tumors

PURPOSE OF REVIEW

In this review, we summarize recent developments in single-cell technologies that can be employed for the functional and molecular classification of endocrine cells in normal and neoplastic tissue.

RECENT FINDINGS

The emergence of new platforms for the isolation, analysis, and dynamic assessment of individual cell identity and reactive behavior enables experimental deconstruction of intratumoral heterogeneity and other contexts where variability in cell signaling and biochemical responsiveness inform biological function and clinical presentation. These tools are particularly appropriate for examining and classifying endocrine neoplasias, as the clinical sequelae of these tumors are often driven by disrupted hormonal responsiveness secondary to compromised cell signaling. Single-cell methods allow for multidimensional experimental designs incorporating both spatial and temporal parameters with the capacity to probe dynamic cell signaling behaviors and kinetic response patterns dependent upon sequential agonist challenge.

SUMMARY

Intratumoral heterogeneity in the provenance, composition, and biological activity of different forms of endocrine neoplasia presents a significant challenge for prognostic assessment. Single-cell technologies provide an array of powerful new approaches uniquely well suited for dissecting complex endocrine tumors. Studies examining the relationship between clinical behavior and tumor compositional variations in cellular activity are now possible, providing new opportunities to deconstruct the underlying mechanisms of endocrine neoplasia.

Monitoring cancer stem cells: insights into clinical oncology

Cancer stem cells (CSCs) are a small, characteristically distinctive subset of tumor cells responsible for tumor initiation and progression. Several treatment modalities, such as surgery, glycolytic inhibition, driving CSC proliferation, immunotherapy, and hypofractionated radiotherapy, may have the potential to eradicate CSCs. We propose that monitoring CSCs is important in clinical oncology as CSC populations may reflect true treatment response and assist with managing treatment strategies, such as defining optimal chemotherapy cycles, permitting pretreatment cancer surveillance, conducting a comprehensive treatment plan, modifying radiation treatment, and deploying rechallenge chemotherapy. Then, we describe methods for monitoring CSCs.

Transcriptome profiling and pathway analysis of hepatotoxicity induced by tris (2-ethylhexyl) trimellitate (TOTM) in mice

Tris (2-ethylhexyl) trimellitate (TOTM) is commonly used as an alternative plasticizer for medical devices. But very little information was available on its biological effects. In this study, we investigated toxicity effects of TOTM on hepatic differential gene expression analyzed by using high-throughput sequencing analysis for over-represented functions and phenotypically anchored to complementary histopathologic, and biochemical data in the liver of mice. Among 1668 candidate genes, 694 genes were up-regulated and 974 genes were down-regulated after TOTM exposure. Using Gene Ontology analysis, TOTM affected three processes: the cell cycle, metabolic process and oxidative activity. Furthermore, 11 key genes involved in the above processes were validated by real time PCR. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that these genes were involved in the cell cycle pathway, lipid metabolism and oxidative process. It revealed the transcriptome gene expression response to TOTM exposure in mouse, and these data could contribute to provide a clearer understanding of the molecular mechanisms of TOTM-induced hepatotoxicity in human.