A highly sensitive and accurate gene expression analysis by sequencing ("bead-seq") for a single cell

Molecular profiles of single circulating tumor cells from early breast cancer patients with different lymph node statuses

BACKGROUND

Characterization of early breast cancer circulating tumor cells (CTCs) may provide valuable information on tumor metastasis.

METHODS

We used immunomagnetic nanospheres to capture CTCs from the peripheral blood of eight early breast cancer patients and then performed single-cell RNA sequencing using our proposed bead-dd-seq method.

RESULTS

CTCs displayed obvious tumor cell characteristics, such as the activation of oxidative stress, proliferation, and promotion of metastasis. CTCs were clustered into two subtypes significantly correlated with the lymph node metastasis status of patients. CTCs in subtype 1 showed a strong metastatic ability because these CTCs have the phenotype of partial epithelial-mesenchymal transition and enriched transcripts, indicating breast cancer responsiveness and proliferation. Furthermore, DNA damage repair pathways were significantly upregulated in subtype 1. We performed in vitro and in vivo investigations, and found that cellular oxidative stress and further DNA damage existed in CTCs. The activated DNA damage repair pathway in CTCs favors resistance to cisplatin. A checkpoint kinase 1 inhibitor sensitized CTCs to cisplatin in mouse models of breast cancer metastasis.

CONCLUSION

The present study dissects the molecular characteristics of CTCs from early-stage breast cancer, providing novel insight into the understanding of CTC behavior in breast cancer metastasis.

TAS-Seq is a robust and sensitive amplification method for bead-based scRNA-seq

Single-cell RNA-sequencing (scRNA-seq) is valuable for analyzing cellular heterogeneity. Cell composition accuracy is critical for analyzing cell–cell interaction networks from scRNA-seq data. However, droplet- and plate-based scRNA-seq techniques have cell sampling bias that could affect the cell composition of scRNA-seq datasets. Here we developed terminator-assisted solid-phase cDNA amplification and sequencing (TAS-Seq) for scRNA-seq based on a terminator, terminal transferase, and nanowell/bead-based scRNA-seq platform. TAS-Seq showed high tolerance to variations in the terminal transferase reaction, which complicate the handling of existing terminal transferase-based scRNA-seq methods. In murine and human lung samples, TAS-Seq yielded scRNA-seq data that were highly correlated with flow-cytometric data, showing higher gene-detection sensitivity and more robust detection of important cell–cell interactions and expression of growth factors/interleukins in cell subsets than 10X Chromium v2 and Smart-seq2. Expanding TAS-Seq application will improve understanding and atlas construction of lung biology at the single-cell level.

Terminator-assisted solid-phase cDNA amplification and sequencing (TASseq) uses a terminator, terminal transferase and bead-based platform to improve generation of single-cell RNA-seq libraries.

Wake-up alarm: virtual time-lapse gene expression landscape illuminates mechanisms underlying dormancy breaking of germinating spores

Dormancy breaking is a common physiological phenomenon that is shared by eukaryotes. Germination of spores in fungi is one of the most representative cases of dormancy breaking. Understanding the mechanisms of spore germination is therefore fundamental to basic studies on the control of cell proliferation and differentiation, as well as agricultural applications and medical investigation of fungal pathogenesis. In fission yeast, spores are generated as a consequence of sexual differentiation under nutrient starvation, remaining dormant until further nourishment, but little is known about how dormant spores germinate in response to environmental change. In a breakthrough, methods for single-cell-based gene expression profiling have recently been introduced. Several mRNA expression profiles were assembled from single spore cells during dormancy or germination. Single-cell RNA-seq profiles were aligned sequentially according to their similarities. The alignment of transcriptomes visualised how gene expression varies over time upon dormancy breaking. In this review, we revisit knowledge from previous studies on germination, select candidate genes that may be involved in germination, and query their expression from the temporal transcriptomic dataset so that studies on S. pombe germination can be extended further.

Single-cell sequencing techniques from individual to multiomics analyses

Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

Combining data from different single-cell sequencing techniques could greatly improve understanding of the molecular profiles associated with disease. Sequencing studies provide valuable insights into diseased and healthy states at a single-cell level, for example the evolutionary paths of brain tumors and cancerous mutations. Ayako Suzuki at the University of Tokyo in Chiba, Japan, and co-workers examined the challenges of integrating data from various experimental and computational single-cell sequencing methods. These methods usually determine the genomic, epigenomic (DNA modifications) or transcriptomic (messenger RNAs) state of a cell, and can be combined to create a detailed picture. Other ‘multiomics’ techniques provide multilayered information from the same cell. The researchers recommend detailed analysis of individual data layers prior to integration, and highlight emerging techniques that analyze larger tissue sections, thus retaining the temporal and spatial information around a cell.

Slow-Cycling Cancer Stem Cells Regulate Progression and Chemoresistance in Colon Cancer

Cancer chemoresistance is often attributed to the presence of cancer stem cell (CSC)-like cells, but whether they are homogeneously chemoresistant remains unclear. We previously showed that in colon tumors, a subpopulation of LGR5⁺ CSC-like cells driven by TCF1 (TCF7), a Wnt-responsive transcription factor, were responsible for tumorigenicity. Here we demonstrate that the tumorigenic subpopulation of mouse LGR5⁺ cells exists in a slow-cycling state and identify a unique 22-gene signature that characterizes these slow-cycling CSC. Seven of the signature genes are specifically expressed in slow-cycling LGR5⁺ cells from xenografted human colon tumors and are upregulated in colon cancer clinical specimens. Among these seven, four genes (APCDD1, NOTUM, PROX1, and SP5) are known to be direct Wnt target genes, and PROX1 was expressed in the invasive fronts of colon tumors. PROX1 was activated by TCF1 to induce CDKN1C and maintain a slow-cycling state in colon cancer organoids. Strikingly, PROX1 was required for recurrent growth after chemotherapeutic treatment, suggesting that inhibition of slow-cycling CSC by targeting the TCF1-PROX1-CDKN1C pathway is an effective strategy to combat refractory colon cancer in combination with conventional chemotherapy. SIGNIFICANCE: These findings illustrate the importance of a slow-cycling CSC subpopulation in colon cancer development and chemoresistance, with potential implications for the identified slow-cycling CSC signatures and the TCF1-PROX1-CDKN1C pathway as therapeutic targets.

Time-lapse single-cell transcriptomics reveals modulation of histone H3 for dormancy breaking in fission yeast

How quiescent cells break dormancy is a key issue in eukaryotic cells including cancer. Fungal spores, for example, remain quiescent for long periods until nourished, although the mechanisms by which dormancy is broken remain enigmatic. Transcriptome analysis could provide a clue, but methods to synchronously germinate large numbers of spores are lacking, and thus it remains a challenge to analyse gene expression upon germination. Hence, we develop methods to assemble transcriptomes from individual, asynchronous spore cells of fission yeast undergoing germination to assess transcriptomic changes over time. The virtual time-lapse analyses highlights one of three copies of histone H3 genes whose transcription fluctuates during the initial stage of germination. Disruption of this temporal fluctuation causes defects in spore germination despite no visible defects in other stages of the life cycle. We conclude that modulation of histone H3 expression is a crucial 'wake-up' trigger at dormancy breaking.

DBTSS/DBKERO for integrated analysis of transcriptional regulation

DBTSS (Database of Transcriptional Start Sites)/DBKERO (Database of Kashiwa Encyclopedia for human genome mutations in Regulatory regions and their Omics contexts) is the database originally initiated with the information of transcriptional start sites and their upstream transcriptional regulatory regions. In recent years, we updated the database to assist users to elucidate biological relevance of the human genome variations or somatic mutations in cancers which may affect the transcriptional regulation. In this update, we facilitate interpretations of disease associated genomic variation, using the Japanese population as a model case. We enriched the genomic variation dataset consisting of the 13,368 individuals collected for various genome-wide association studies and the reference epigenome information in the surrounding regions using a total of 455 epigenome datasets (four tissue types from 67 healthy individuals) collected for the International Human Epigenome Consortium (IHEC). The data directly obtained from the clinical samples was associated with that obtained from various model systems, such as the drug perturbation datasets using cultured cancer cells. Furthermore, we incorporated the results obtained using the newly developed analytical methods, Nanopore/10x Genomics long-read sequencing of the human genome and single cell analyses. The database is made publicly accessible at the URL (http://dbtss.hgc.jp/).

An Informative Approach to Single-Cell Sequencing Analysis

Recent advances in sequencing technologies enable us to obtain genome, epigenome and transcriptome data in individual cells. In this review, we describe various platforms for single-cell sequencing analysis across multiple layers. We mainly introduce an automated single-cell RNA-seq platform, the Chromium Single Cell 3' RNA-seq system, and its technical features and compare it with other single-cell RNA-seq systems. We also describe computational methods for analyzing large, complex single-cell datasets. Due to the insufficient depth of single-cell RNA-seq data, resulting in a critical lack of transcriptome information for low-expressed genes, it is occasionally difficult to interpret the data as is. To overcome the analytical problems for such sparse datasets, there are many bioinformatics reports that provide informative approaches, including imputation, correction of batch effects, dimensional reduction and clustering.

Strategies for Converting RNA to Amplifiable cDNA for Single-Cell RNA Sequencing Methods

This review describes the features of molecular biology techniques for single-cell RNA sequencing (scRNA-seq), including methods developed in our laboratory. Existing scRNA-seq methods require the conversion of first-strand cDNA to amplifiable cDNA followed by whole-transcript amplification. There are three primary strategies for this conversion: poly-A tagging, template switching, and RNase H-DNA polymerase I-mediated second-strand cDNA synthesis for in vitro transcription. We discuss the merits and limitations of these strategies and describe our Reverse Transcription with Random Displacement Amplification technology that allows for direct first-strand cDNA amplification from RNA without the need for conversion to an amplifiable cDNA. We believe that this review provides all users of single-cell transcriptome technologies with an understanding of the relationship between the quantitative performance of various methods and their molecular features.

Electrochemical Biosensing System for Single Cells, Cellular Aggregates and Microenvironments

Applications of electrochemical biosensing for surveying intact cells and tissues have been focus of attention. Two experimental approaches have been used when performing amperometric measurements on biological cells, the stylus-type microelectrode probes and the electrode-integrated microdevices based on lithographic technologies. For the probe scanning approach, various types of microsensors were developed to monitor localized physical or chemical natures at a variety of surfaces in situ under wet conditions. Scanning electrochemical microscopy (SECM) has been applied for monitoring local oxygen, enzyme activity, and collection of transcripts. For the non-scanning type of approach, electrode array devices allow very rapid response, parallel monitoring, and multi-analyte assay. Sveral topics of on-chip-culture system were introduced especially concerning on gene expression monitoring by reporter system and reconstruction of in vivo-like nature by controlling microenvironments. Electrochemical reporter assay has been demonstrated to monitor the gene expression process of the gene-modified cultured cells. Long-term monitoring of cellular function of spheroids and three dimensionally-cultured cells were carried out by controlling microenvironments on the cellular chip.

Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response

Single-cell RNA-seq is a powerful tool for revealing heterogeneity in cancer cells. However, each of the current single-cell RNA-seq platforms has inherent advantages and disadvantages. Here, we show that combining the different single-cell RNA-seq platforms can be an effective approach to obtaining complete information about expression differences and a sufficient cellular population to understand transcriptional heterogeneity in cancers. We demonstrate that it is possible to estimate missing expression information. We further demonstrate that even in the cases where precise information for an individual gene cannot be inferred, the activity of given transcriptional modules can be analyzed. Interestingly, we found that two distinct transcriptional modules, one associated with the Aurora kinase gene and the other with the DUSP gene, are aberrantly regulated in a minor population of cells and may thus contribute to the possible emergence of dormancy or eventual drug resistance within the population.

Application of next-generation sequencing methods for microbial monitoring of anaerobic digestion of lignocellulosic biomass

The anaerobic digestion of lignocellulosic wastes is considered an efficient method for managing the world's energy shortages and resolving contemporary environmental problems. However, the recalcitrance of lignocellulosic biomass represents a barrier to maximizing biogas production. The purpose of this review is to examine the extent to which sequencing methods can be employed to monitor such biofuel conversion processes. From a microbial perspective, we present a detailed insight into anaerobic digesters that utilize lignocellulosic biomass and discuss some benefits and disadvantages associated with the microbial sequencing techniques that are typically applied. We further evaluate the extent to which a hybrid approach incorporating a variation of existing methods can be utilized to develop a more in-depth understanding of microbial communities. It is hoped that this deeper knowledge will enhance the reliability and extent of research findings with the end objective of improving the stability of anaerobic digesters that manage lignocellulosic biomass.

Site-specific gene expression analysis using an automated tissue micro-dissection punching system

Site-specific gene expression analyses are important for understanding tissue functions. Despite rapid developments in DNA-related technologies, the site-specific analysis of whole genome expression for a tissue remains challenging. Thus, a new tool is required for capturing multiple tissue micro-dissections or single cells while retaining the positional information. Here, we describe the development of such a system, which can pick up micro-dissections by punching a tissue repeatedly in a very short period, e.g., 5 s/sampling cycle. A photo of the punched tissue provides information on the dissected positions, allowing site-specific gene expression analysis. We demonstrate the site-specific analysis of a frozen tissue slice of mouse brain by analyzing many micro-dissections produced from the tissue at a 300-μm pitch. The site-specific analysis provided new insights into the gene expression profiles in a tissue and on tissue functions. The analysis of site-specific whole genome expression may therefore, open new avenues in life science.

Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments

Single-cell mRNA sequencing offers an unbiased approach to dissecting cell types as functional units in multicellular tissues. However, highly reliable cell typing based on single-cell gene expression analysis remains challenging because of the lack of methods for efficient sample preparation for high-throughput sequencing and evaluating the statistical reliability of the acquired cell types. Here, we present a highly efficient nucleic reaction chip (a vertical flow array chip (VFAC)) that uses porous materials to reduce measurement noise and improve throughput without a substantial increase in reagent. We also present a probabilistic evaluation method for cell typing depending on the amount of measurement noise. Applying the VFACs to 2580 monocytes provides 1967 single-cell expressions for 47 genes, including low-expression genes such as transcription factors. The statistical method can distinguish two cell types with probabilistic quality values, with the measurement noise level being considered for the first time. This approach enables the identification of various sub-types of cells in tissues and provides a foundation for subsequent analyses.

Position-Specific Gene Expression Analysis Using a Microgram Dissection Method Combined with On-Bead cDNA Library Construction

Gene expression analysis is a key technology that is used to understand living systems. Multicellular organisms, including plants, are composed of various tissues and cell types, each of which exhibits a unique gene expression pattern. However, because of their rigid cell walls, plant cells are difficult to isolate from the whole plant. Although laser dissection has been used to circumvent this problem, the plant sample needs to be fixed beforehand, which presents several problems. In the present study, we developed an alternative method to conduct highly reliable gene expression profiling. First, we assembled a dissection apparatus that used a narrow, sharpened needle to dissect out a microsample of fresh plant tissue (0.1-0.2 mm on each side) automatically from a target site within a short time frame. Then, we optimized a protocol to synthesize a high-quality cDNA library on magnetic beads using a single microsample. The cDNA library was amplified and subjected to high-throughput sequencing. In this way, a stable and reliable system was developed to conduct gene expression profiling in small regions of a plant. The system was used to analyze the gene expression patterns at successive 50 µm intervals in the shoot apex of a 4-day-old Arabidopsis seedling. Clustering analysis of the data demonstrated that two small, adjacent domains, the shoot apical meristem and the leaf primordia, were clearly distinguishable. This system should be broadly applicable in the investigation of the spatial organization of gene expression in various contexts.