Application of single-cell technology in cancer research

Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities

Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.

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.

Introducing single cell stereo-sequencing technology to transform the plant transcriptome landscape

Single cell RNA-sequencing (scRNA-seq) advancements have helped detect transcriptional heterogeneities in biological samples. However, scRNA-seq cannot currently provide high-resolution spatial transcriptome information or identify subcellular organs in biological samples. These limitations have led to the development of spatially enhanced-resolution omics-sequencing (Stereo-seq), which combines spatial information with single cell transcriptomics to address the challenges of scRNA-seq alone. In this review, we discuss the advantages of Stereo-seq technology. We anticipate that the application of such an integrated approach in plant research will advance our understanding of biological process in the plant transcriptomics era. We conclude with an outlook of how such integration will enhance crop improvement.

Synchrotron Fourier-Transform Infrared Microspectroscopy: Characterization of in vitro polarized tumor-associated macrophages stimulated by the secretome of inflammatory and non-inflammatory breast cancer cells

Studies suggested that the pathogenesis of inflammatory breast cancer (IBC) is related to inflammatory manifestations accompanied by specific cellular and molecular mechanisms in the IBC tumor microenvironment (TME). IBC is characterized by significantly higher infiltration of tumor-associated macrophages (TAMs) that contribute to its metastatic process via secreting many cytokines such as TNF, IL-6, IL-8, and IL-10 that enhance invasion and angiogenesis. Thus, there is a need to first understand how IBC-TME modulates the polarization of TAMs to better understand the role of TAMs in IBC. Herein, we used gene expression signature and Synchrotron Fourier-Transform Infrared Microspectroscopy (SR-μFTIR) to study the molecular and biochemical changes, respectively of in vitro polarized TAMs stimulated by the secretome of IBC and non-IBC cells. The gene expression signature showed significant differences in the macrophage's polarization-related genes between stimulated TAMs. FTIR spectra showed absorption bands in the region of 1700-1500 cm^-1 attributed to the amide I ν(C=O), & ν_AS (CN), δ (NH), and amide II ν(CN), δ (NH) proteins bands. Moreover, three peaks of different intensities and areas were detected in the lipid region of the νCH₂ and νCH₃ stretching modes positioned within the 3000-2800 cm^-1 range. The PCA analysis for the second derivative spectra of the amide regions discriminates between stimulated IBC and non-IBC TAMs. This study showed that IBC and non-IBC TMEs differentially modulate the polarization of TAMs and SR-μFTIR can determine these biochemical changes which will help to better understand the potential role of TAMs in IBC.

Transforming Diagnosis and Therapeutics Using Cancer Genomics

In past quarter of the century, much has been understood about the genetic variation and abnormal genes that activate cancer in humans. All the cancers somehow possess alterations in the DNA sequence of cancer cell's genome. In present, we are heading toward the era where it is possible to obtain complete genome of the cancer cells for their better diagnosis, categorization and to explore treatment options.

An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis

Hepatocellular carcinoma is a primary liver cancer caused by the accumulation of genetic mutation patterns associated with epidemiological conditions. This lethal malignancy exhibits tumor heterogeneity, which is considered as one of the main reasons for drug resistance development and failure of clinical trials. Recently, single-cell technology (SCT), a new advanced sequencing technique that analyzes every single cell in a tumor tissue specimen, aids complete insight into the genetic heterogeneity of cancer. This helps in identifying and assessing rare cell populations by analyzing the difference in gene expression pattern between individual cells of single biopsy tissue which normally cannot be identified from pooled cell gene expression pattern (traditional sequencing technique). Thus, SCT improves the clinical diagnosis, treatment, and prognosis of hepatocellular carcinoma as the limitations of other techniques impede this cancer research progression. Application of SCT at the genomic, transcriptomic, and epigenomic levels to promote individualized hepatocellular carcinoma diagnosis and therapy. The current review has been divided into ten sections. Herein we deliberated on the SCT, hepatocellular carcinoma diagnosis, tumor microenvironment analysis, single-cell genomic sequencing, single-cell transcriptomics, single-cell omics sequencing for biomarker development, identification of hepatocellular carcinoma origination and evolution, limitations, challenges, conclusions, and future perspectives.

Tumor Heterogeneity and Consequences for Bladder Cancer Treatment

Simple Summary

Bladder cancer is a heterogeneous disease that is composed of epithelia with varying transcriptional, mutational and lineage signatures. The epithelia of bladder tumors can also undergo pronounced changes in transcriptional and phenotypical qualities in response to progression, treatment related stresses and cues from the tumor microenvironment (TME). We hypothesize that changes in epithelial tumor heterogeneity (EpTH) occur due to the evolving content of epithelial subpopulations through both Darwinian and Lamarckian-like natural selection processes. We further conjecture that lineage-defined subpopulations can change through nongenomic and genomic cellular mechanisms that include cellular plasticity and acquired driver mutations, respectively. We propose that such processes are dynamic and contribute towards clinical treatment challenges including progression to drug resistance. In this article, we assess mechanisms that may support dynamic tumor heterogeneity with the overall goal of emphasizing the application of these concepts to the clinical setting.

Abstract

Acquired therapeutic resistance remains a major challenge in cancer management and associates with poor oncological outcomes in most solid tumor types. A major contributor is tumor heterogeneity (TH) which can be influenced by the stromal; immune and epithelial tumor compartments. We hypothesize that heterogeneity in tumor epithelial subpopulations—whether de novo or newly acquired—closely regulate the clinical course of bladder cancer. Changes in these subpopulations impact the tumor microenvironment including the extent of immune cell infiltration and response to immunotherapeutics. Mechanisms driving epithelial tumor heterogeneity (EpTH) can be broadly categorized as mutational and non-mutational. Mechanisms regulating lineage plasticity; acquired cellular mutations and changes in lineage-defined subpopulations regulate stress responses to clinical therapies. If tumor heterogeneity is a dynamic process; an increased understanding of how EpTH is regulated is critical in order for clinical therapies to be more sustained and durable. In this review and analysis, we assess the importance and regulatory mechanisms governing EpTH in bladder cancer and the impact on treatment response.

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence

Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.

Recent advances in single-cell analysis: Encapsulation materials, analysis methods and integrative platform for microfluidic technology

Traditional cell biology researches on cell populations by their origin, tissue, morphology, and secretions. Because of the heterogeneity of cells, research at the single-cell level can obtain more accurate and comprehensive information that reflects the physiological state and process of the cell, increasing the significance of single-cell analysis. The application of single-cell analysis is faced with the problem of contaminated or damaged cells caused by cell sample transportation. Reversible encapsulation of a single cell can protect cells from the external environment and open the encapsulation shell to release cells, thus preserving cell integrity and improving extraction efficiency of analytes. Meanwhile, microfluidic single cell analysis (MSCA) exhibits integration, miniaturization, and high throughput, which can considerably improve the efficiency of single-cell analysis. The researches on single-cell reversible encapsulation materials, single-cell analysis methods, and the MSCA integration platform are analyzed and summarized in this review. The problems of single-cell viability, network of single-cell signal, and simultaneous detection of multiple biotoxins in food based on single-cell are proposed for future research.

Cancer stem cell characteristics and their potential as therapeutic targets

Cancer stem cells (CSCs) are a tumour subpopulation whose capacity for self-renewal, differentiation and proliferation generates unfavourable patient outcomes, including therapeutic resistance and metastasis. Much research has focused on the generation, biomarkers and therapeutic resistance of CSCs, as well as the development of CSC-targeted therapies. Reviews to date have either addressed general CSC characteristics or focused on CSCs from a well-studied cancer. Increasingly, specific treatment plans based on identification of molecular features and biomarkers of a patient's cancer, rather than classification according to tissue origin or bulk tumour properties, are leading to better patient outcomes. Here, we compare CSC characteristics, specifically their biomarkers and molecular features, and identify those that are common to a number of cancers. Identification of CSC markers that suggest therapeutic strategies has led to several successful in vitro and animal tests, recommending clinical trials of treatments with potentially enhanced therapeutic benefits, especially for recurring cancers.

Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.

Biological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Due to the technical limitations, spatial transcriptomics data suffers from only sparsely measured mRNAs by in-situ capture and possibly missing spots in tissue regions that entirely failed fixing and permeabilizing RNAs. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.

The key roles of cancer stem cell-derived extracellular vesicles

Cancer stem cells (CSCs), the subpopulation of cancer cells, have the capability of proliferation, self-renewal, and differentiation. The presence of CSCs is a key factor leading to tumor progression and metastasis. Extracellular vesicles (EVs) are nano-sized particles released by different kinds of cells and have the capacity to deliver certain cargoes, such as nucleic acids, proteins, and lipids, which have been recognized as a vital mediator in cell-to-cell communication. Recently, more and more studies have reported that EVs shed by CSCs make a significant contribution to tumor progression. CSCs-derived EVs are involved in tumor resistance, metastasis, angiogenesis, as well as the maintenance of stemness phenotype and tumor immunosuppression microenvironment. Here, we summarized the molecular mechanism by which CSCs-derived EVs in tumor progression. We believed that the fully understanding of the roles of CSCs-derived EVs in tumor development will definitely provide new ideas for CSCs-based therapeutic strategies.

PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

SUMMARY

Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.

AVAILABILITY AND IMPLEMENTATION

The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell transcriptome conservation in a comparative analysis of fresh and cryopreserved human skin tissue: pilot in localized scleroderma

Background

The purpose of this study was to assess variability in cell composition and cell-specific gene expression in the skin of patients with localized scleroderma (LS) utilizing CryoStor® CS10 in comparison to RPMI to produce adequate preservation of tissue samples and cell types of interest for use in large-scale multi-institutional collaborations studying localized scleroderma and other skin disorders.

Methods

We performed single-cell RNA sequencing on paired skin biopsy specimens from 3 patients with LS. Each patient with one sample cryopreserved in CryoStor® CS10 and one fresh in RPMI media using 10× Genomics sequencing.

Results

Levels of cell viability and yield were comparable between CryoStor® CS10 (frozen) and RPMI (fresh) preserved cells. Furthermore, gene expression between preservation methods was collectively significantly correlated and conserved across all 18 identified cell cluster populations.

Conclusion

Comparable cell population and transcript expression yields between CryoStor® CS10 and RPMI preserved cells support the utilization of cryopreserved skin tissue in single-cell analysis. This suggests that employing standardized cryopreservation protocols for the skin tissue will help facilitate multi-site collaborations looking to identify mechanisms of disease in disorders characterized by cutaneous pathology.

Urinary Biomarkers and Their Potential for the Non-Invasive Detection of Endometrial Cancer

Endometrial cancer is the most common malignancy of the female genital tract and its incidence is rising in parallel with the mounting prevalence of obesity. Early diagnosis has great potential to improve outcomes as treatment can be curative, especially for early stage disease. Current tests and procedures for diagnosis are limited by insufficient accuracy in some and unacceptable levels of invasiveness and discomfort in others. There has, therefore, been a growing interest in the search for sensitive and specific biomarkers for endometrial cancer detection based on non-invasive sampling methodologies. Urine, the prototype non-invasive sample, is attractive for biomarker discovery as it is easily accessible and can be collected repeatedly and in quantity. Identification of urinary biomarkers for endometrial cancer detection relies on the excretion of systemic biomarkers by the kidneys or urinary contamination by biomarkers shed from the uterus. In this review, we present the current standing of the search for endometrial cancer urinary biomarkers based on cytology, genomic, transcriptomic, proteomic, and metabolomic platforms. We summarize the biomarker candidates and highlight the challenges inherent in urinary biomarker discovery. We review the various technologies with promise for biomarker detection and assess these novel approaches for endometrial cancer biomarker research.

The Use of Molecular Subtypes for Precision Therapy of Recurrent and Metastatic Gastrointestinal Stromal Tumor

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumor in the digestive tract. Tyrosine kinase inhibitors (TKIs), represented by imatinib, sunitinib, and regorafenib, have become the main treatment for recurrent and metastatic GISTs. With the wide application of mutation analysis and the precision medicine, molecular characteristics have been determined that not only predict the prognosis of patients with recurrent and metastatic GISTs, but also are closely related to the efficacy of first-, second- and third-line TKIs for GISTs, as well as other TKIs. Despite the significant effects of TKIs, the emergence of primary and secondary resistance ultimately leads to treatment failure and tumor progression. Currently, due to the signal transmission of KIT/PDGFRA during onset and tumor progression, strategies to counteract drug resistance include the replacement of TKIs and the development of new drugs that are directed towards carcinogenic mutations. In addition, it is also the embodiment of precision medicine for GISTs to explore new carcinogenic mechanisms and develop new drugs relying on new biotechnology. Surgery can benefit specific patients but its major purpose is to diminish the resistant clones. However, the prognosis of recurrent and metastatic patients is still unsatisfactory. Therefore, it is worth paying attention to how to maximize the benefits for patients.

Micro RNA Molecules as Modulators of Treatment Resistance, Immune Checkpoints Controllers and Sensitive Biomarkers in Glioblastoma Multiforme

Based on genome sequencing, it is estimated that over 90% of genes stored in human genetic material are transcribed, but only 3% of them contain the information needed for the production of body proteins. This group also includes micro RNAs representing about 1%–3% of the human genome. Recent studies confirmed the hypothesis that targeting molecules called Immune Checkpoint (IC) open new opportunities to take control over glioblastoma multiforme (GBM). Detection of markers that indicate the presence of the cancer occupies a very important place in modern oncology. This function can be performed by both the cancer cells themselves as well as their components and other substances detected in the patients’ bodies. Efforts have been made for many years to find a suitable marker useful in the diagnosis and monitoring of gliomas, including glioblastoma.

Gating a Single Cell: A Label-Free and Real-Time Measurement Method for Cellular Progression

There is an ever-growing need for more advanced methods to study the response of cancer cells to new therapies. To determine cancer cells' response from a cell-mortality perspective to various cancer therapies, we report a label-free and real time method to monitor the in situ response of individual HeLa cells using a single cell gated transistor (SCGT). As a cell undergoes apoptotic cell death, it experiences changes in morphology and ion concentrations. This change is well in line with the threshold voltage of the SCGT, which has been verified by correlating the data with the cell morphologies by scanning electron microscopy and the ion-concentration analysis by inductively-coupled plasma mass spectrometry (ICPMS). This SCGT could replace patch clamps to study single cell activity via direct measurement in real time. Importantly, this SCGT can be used to study the electrical response of a single cell to stimuli that leaves the membrane intact.

Cytogenetics and Cytogenomics Evaluation in Cancer

The availability of cytogenetics and cytogenomics technologies improved the detection and identification of tumor molecular signatures as well as the understanding of cancer initiation and progression. The use of large-scale and high-throughput cytogenomics technologies has led to a fast identification of several cancer candidate biomarkers associated with diagnosis, prognosis, and therapeutics. The advent of array comparative genomic hybridization and next-generation sequencing technologies has significantly improved the knowledge about cancer biology, underlining driver genes to guide targeted therapy development, drug-resistance prediction, and pharmacogenetics. However, few of these candidate biomarkers have made the transition to the clinic with a clear benefit for the patients. Technological progress helped to demonstrate that cellular heterogeneity plays a significant role in tumor progression and resistance/sensitivity to cancer therapies, representing the major challenge of precision cancer therapy. A paradigm shift has been introduced in cancer genomics with the recent advent of single-cell sequencing, since it presents a lot of applications with a clear benefit to oncological patients, namely, detection of intra-tumoral heterogeneity, mapping clonal evolution, monitoring the development of therapy resistance, and detection of rare tumor cell populations. It seems now evident that no single biomarker could provide the whole information necessary to early detect and predict the behavior and prognosis of tumors. The promise of precision medicine is based on the molecular profiling of tumors being vital the continuous progress of high-throughput technologies and the multidisciplinary efforts to catalogue chromosomal rearrangements and genomic alterations of human cancers and to do a good interpretation of the relation genotype-phenotype.

Cancer Stem Cells in Head and Neck Carcinomas: Identification and Possible Therapeutic Implications

The recurrence and/or lack of response of certain tumors to radio- and chemotherapy has been attributed to a small subpopulation of cells termed cancer stem cells (CSCs). CSCs have been identified in many tumors (including solid and hematological tumors). CSCs are characterized by their capacity for self-renewal, their ability to introduce heterogeneity within a tumor mass and its metastases, genomic instability, and their insensitivity to both radiation and chemotherapy. The latter highlights the clinical importance of studying this subpopulation since their resistance to traditional treatments may lead to metastatic disease and/or tumor relapse. Head and neck squamous cell carcinomas (HNSCCs) are the sixth most common malignancy worldwide with the highest incidence occurring in East Asia and eastern and southern Africa. Several cellular subpopulations believed to have CSC properties have been isolated from HNSCCs, but at present, identification and characterization of CSCs remains an experimental challenge with no established or standardized protocols in place to confirm their identity. In this review we discuss current approaches to the study of CSCs with a focus on HNSCCs, particularly in the context of what this might mean from a therapeutic perspective.

Single-Cell Omics Analyses Enabled by Microchip Technologies

Single-cell omics studies provide unique information regarding cellular heterogeneity at various levels of the molecular biology central dogma. This knowledge facilitates a deeper understanding of how underlying molecular and architectural changes alter cell behavior, development, and disease processes. The emerging microchip-based tools for single-cell omics analysis are enabling the evaluation of cellular omics with high throughput, improved sensitivity, and reduced cost. We review state-of-the-art microchip platforms for profiling genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multi-omics at single-cell resolution. We also discuss the background of and challenges in the analysis of each molecular layer and integration of multiple levels of omics data, as well as how microchip-based methodologies benefit these fields. Additionally, we examine the advantages and limitations of these approaches. Looking forward, we describe additional challenges and future opportunities that will facilitate the improvement and broad adoption of single-cell omics in life science and medicine.

On-chip anticancer drug screening - Recent progress in microfluidic platforms to address challenges in chemotherapy

There is an increasing need for advanced and inexpensive preclinical models to accelerate the development of anticancer drugs. While costly animal models fail to predict human clinical outcomes, in vitro models such as microfluidic chips ('tumor-on-chip') are showing tremendous promise at predicting and providing meaningful preclinical drug screening outcomes. Research on 'tumor-on-chips' has grown enormously worldwide and is being widely accepted by pharmaceutical companies as a drug development tool. In light of this shift in philosophy, it is important to review the recent literature on microfluidic devices to determine how rapidly the technology has progressed as a promising model for drug screening and aiding cancer therapy. We review the past five years of successful developments and capabilities in microdevice technology (cancer models) for use in anticancer drug screening. Microfluidic devices that are being designed to address current challenges in chemotherapy, such as drug resistance, combinatorial drug therapy, personalized medicine, and cancer metastasis are also reviewed in detail. We provide a perspective on how personalized 'tumor-on-chip', as well as high-throughput microfluidic platforms based on patient-specific tumor cells, can potentially replace the more expensive and 'non-human' animal models in preclinical anticancer drug development.

The Impact of Heterogeneity on Single-Cell Sequencing

The importance of diversity and cellular specialization is clear for many reasons, from population-level diversification, to improved resiliency to unforeseen stresses, to unique functions within metazoan organisms during development and differentiation. However, the level of cellular heterogeneity is just now becoming clear through the integration of genome-wide analyses and more cost effective Next Generation Sequencing (NGS). With easy access to single-cell NGS (scNGS), new opportunities exist to examine different levels of gene expression and somatic mutational heterogeneity, but these assays can generate yottabyte scale data. Here, we model the importance of heterogeneity for large-scale analysis of scNGS data, with a focus on the utilization in oncology and other diseases, providing a guide to aid in sample size and experimental design.

Au-aided reduced graphene oxide-based nanohybrids for photo-chemotherapy

Graphene-based nanomaterials show great potential in photo-chemotherapy, but their photo-thermal effect is not very satisfactory. Herein, we presented a facile and low-cost strategy to grow Au clusters on the reduced graphene oxide (rGO) sheets aiming to improve photothermal effect. Au clusters with low-concentration was directly conjugated on the surface of rGO by electrostatic forces. To improve its biocompatibility, 3‑(3‑phenylureido) propanoic acid (PPA)-PEG (PPEG) had been introduced as biodegradable backbone to form rGO/Au/PPEG nanohybrids via π-π accumulation. The obtained rGO-based nanohybrids showed excellent biocompatibility, stability, low cytotoxicity, and enhanced photo-thermal conversion efficiency. To verify the synergistic photo-chemotherapy, doxorubicin (DOX) as a drug model had been loaded in rGO/Au/PPEG nanohybrids. The results indicated that rGO/Au/PPEG/DOX exhibited synergistic therapeutic efficacy compared with single chemotherapy or photothermal therapy, endowing this designed rGO-based nanohybrids with great potential for cancer treatments.

Current Trends of Microfluidic Single-Cell Technologies

The investigation of human disease mechanisms is difficult due to the heterogeneity in gene expression and the physiological state of cells in a given population. In comparison to bulk cell measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. In this review, we describe the recent advances in single-cell technologies and their applications in single-cell manipulation, diagnosis, and therapeutics development.

The role of automated cytometry in the new era of cancer immunotherapy

The introduction in the clinical practice of several new approaches to cancer immunotherapy has greatly increased the interest in analytical methodologies that can define the immunological profile of patients in the clinical setting. This requires huge effort to obtain reliable monitoring tools that could be used to improve the patient's clinical outcome. The clinical applications of flow cytometry (FCM) in oncology started with the measurement of DNA content for the evaluation of both ploidy and cell cycle profile as potential prognostic parameters in the majority of human solid cancer types. The availability of monoclonal antibodies widely broadened the spectrum of clinical applications of this technique, which rapidly became a fundamental tool for the diagnosis and prognosis of malignant hematological diseases. Among the emerging clinical applications of FCM, the study of minimal residual disease in hematological malignancies, the quantification of blood dendritic cells in various types of tumors, the study of metastatic spread in solid tumors throughout both the analysis of circulating endothelial progenitor cells and the identification and characterization of circulating tumor cells, all appear very promising. More recently, an advanced single cell analysis technique has been developed that combines the precision of mass spectrometry with the unique advantages of FCM. This approach, termed mass cytometry, utilizes antibodies conjugated to heavy metal ions for the analysis of cellular proteins by a mass spectrometer. It provides measurement of over 100 simultaneous cellular parameters in a single sample and has evolved from a promising technology to a high recognized platform for multi-dimensional single-cell analysis. Should a careful standardization of the analytical procedures be reached, both FCM and mass cytometry could effectively become ideal tools for the optimization of new immunotherapeutic approaches in cancer patients.

Enhancement of cell growth and phycocyanin production in Arthrospira (Spirulina) platensis by metabolic stress and nitrate fed-batch

Arthrospira (Spirulina) platensis is known to have high-quality proteins content and phycocyanin as one of the major pigment constituents of the cells, and the most challenging problem associated with phycocyanin production in Arthrospira is to optimize its intracellular accumulation. The present study evaluated the metabolic stress conditions (by nutrient enrichment) of Arthrospira platensis FACHB-314 for boosting biomass growth and high content phycocyanin accumulation. Experimental results showed that 5 mM sodium glutamate and 7.5 mM succinic acid could enhance biomass yield as well as phycocyanin accumulation compared with that of the control groups. The present study demonstrates that the biomass growth and phycocyanin accumulation were significantly enhanced in fed-batch cultivation of Arthrospira platensis by applying the substrates as metabolic stress agents combined with nitrate feeding strategy. cobA/hemD, hemG and ho genes presented the over-expression level with adding sodium glutamate and succinic acid in cultures, respectively, compared to the control groups.

Lab-on-a-Chip Platforms for Biophysical Studies of Cancer with Single-Cell Resolution

Recent cancer research has more strongly emphasized the biophysical aspects of tumor development, progression, and microenvironment. In addition to genetic modifications and mutations in cancer cells, it is now well accepted that the physical properties of cancer cells such as stiffness, electrical impedance, and refractive index vary with tumor progression and can identify a malignant phenotype. Moreover, cancer heterogeneity renders population-based characterization techniques inadequate, as individual cellular features are lost in the average. Hence, platforms for fast and accurate characterization of biophysical properties of cancer cells at the single-cell level are required. Here, we highlight some of the recent advances in the field of cancer biophysics and the development of lab-on-a-chip platforms for single-cell biophysical analyses of cancer cells.

Nanotechnology and Glycosaminoglycans: Paving the Way Forward for Ovarian Cancer Intervention

Ovarian cancer (OC) has gained a great deal of attention due to its aggressive proliferative capabilities, high death rates and poor treatment outcomes, rendering the disease the ultimate lethal gynaecological cancer. Nanotechnology provides a promising avenue to combat this malignancy by the niche fabrication of optimally-structured nanomedicines that ensure potent delivery of chemotherapeutics to OC, employing nanocarriers to act as "intelligent" drug delivery vehicles, functionalized with active targeting approaches for precision delivery of chemotherapeutics to overexpressed biomarkers on cancer cells. Recently, much focus has been implemented to optimize these active targeting mechanisms for treatment/diagnostic purposes employing nanocarriers. This two-part article aims to review the latest advances in active target-based OC interventions, where the impact of the newest antibody, aptamer and folate functionalization on OC detection and treatment is discussed in contrast to the limitations of this targeting mechanism. Furthermore, we discuss the latest advances in nanocarrier based drug delivery in OC, highlighting their commercial/clinical viability of these systems beyond the realms of research. Lastly, in the second section of this review, we comprehensively discussed a focus shift in OC targeting from the well-studied OC cells to the vastly neglected extracellular matrix and motivate the potential for glycosaminoglycans (GAGs) as a more focused extracellular molecular target.

MicroRNA in Glioblastoma: An Overview

Glioblastoma is the most aggressive brain tumor and, even with the current multimodal therapy, is an invariably lethal cancer with a life expectancy that depends on the tumor subtype but, even in the most favorable cases, rarely exceeds 2 years. Epigenetic factors play an important role in gliomagenesis, are strong predictors of outcome, and are important determinants for the resistance to radio- and chemotherapy. The latest addition to the epigenetic machinery is the noncoding RNA (ncRNA), that is, RNA molecules that are not translated into a protein and that exert their function by base pairing with other nucleic acids in a reversible and nonmutational mode. MicroRNAs (miRNA) are a class of ncRNA of about 22 bp that regulate gene expression by binding to complementary sequences in the mRNA and silence its translation into proteins. MicroRNAs reversibly regulate transcription through nonmutational mechanisms; accordingly, they can be considered as epigenetic effectors. In this review, we will discuss the role of miRNA in glioma focusing on their role in drug resistance and on their potential applications in the therapy of this tumor.