Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing. Proc Natl Acad Sci U S A. 2014;111:E4726-E4735. [PMID: 25339441 DOI: 10.1073/pnas.1404656111]

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Purinergic Ca2+ Signaling as a Novel Mechanism of Drug Tolerance in BRAF-Mutant Melanoma

Drug tolerance is a major cause of relapse after cancer treatment. Despite intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels) and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma and points to actionable strategies to overcome it.

Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most frequent leukemia in adults with a high mortality rate. Current diagnostic criteria and selections of therapeutic strategies are generally based on gene mutations and cytogenetic abnormalities. Chemotherapy, targeted therapies, and hematopoietic stem cell transplantation (HSCT) are the major therapeutic strategies for AML. Two dilemmas in the clinical management of AML are related to its poor prognosis. One is the inaccurate risk stratification at diagnosis, leading to incorrect treatment selections. The other is the frequent resistance to chemotherapy and/or targeted therapies. Genomic features have been the focus of AML studies. However, the DNA-level aberrations do not always predict the expression levels of genes and proteins and the latter is more closely linked to disease phenotypes. With the development of high-throughput sequencing and mass spectrometry technologies, studying downstream effectors including RNA, proteins, and metabolites becomes possible. Transcriptomics can reveal gene expression and regulatory networks, proteomics can discover protein expression and signaling pathways intimately associated with the disease, and metabolomics can reflect precise changes in metabolites during disease progression. Moreover, omics profiling at the single-cell level enables studying cellular components and hierarchies of the AML microenvironment. The abundance of data from different omics layers enables the better risk stratification of AML by identifying prognosis-related biomarkers, and has the prospective application in identifying drug targets, therefore potentially discovering solutions to the two dilemmas. In this review, we summarize the existing AML studies using omics methods, both separately and combined, covering research fields of disease diagnosis, risk stratification, prognosis prediction, chemotherapy, as well as targeted therapy. Finally, we discuss the directions and challenges in the application of multi-omics in precision medicine of AML. Our review may inspire both omics researchers and clinical physicians to study AML from a different angle.

Purinergic Ca2+ signaling as a novel mechanism of drug tolerance in BRAF mutant melanoma

Drug tolerance is a major cause of relapse after cancer treatment. In spite of intensive efforts1-9, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels), and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma, and points to actionable strategies to overcome it.

Spatial transcriptomics data and analytical methods: An updated perspective

Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.

XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures

Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achieving personalized treatment. Despite the increasing abundance of cancer genomics data, predicting the relationship between cancer samples and drug sensitivity remains challenging. In this study, we developed an explainable graph neural network framework for predicting cancer drug sensitivity (XGraphCDS) based on comparative learning by integrating cancer gene expression information and drug chemical structure knowledge. Specifically, XGraphCDS consists of a unified heterogeneous network and multiple sub-networks, with molecular graphs representing drugs and gene enrichment scores representing cell lines. Experimental results showed that XGraphCDS consistently outperformed most state-of-the-art baselines (R2 = 0.863, AUC = 0.858). We also constructed a separate in vivo prediction model by using transfer learning strategies with in vitro experimental data and achieved good predictive power (AUC = 0.808). Simultaneously, our framework is interpretable, providing insights into resistance mechanisms alongside accurate predictions. The excellent performance of XGraphCDS highlights its immense potential in aiding the development of selective anti-tumor drugs and personalized dosing strategies in the field of precision medicine.

Exploring the mechanism underlying the therapeutic effects of butein in colorectal cancer using network pharmacology and single-cell RNA sequencing data

BACKGROUND

Butein has shown substantial potential as a cancer treatment, but its precise mechanism of action in colorectal cancer (CRC) remains unclear. This study aimed to uncover the underlying mechanisms through which butein operates in CRC and to identify potential biomarkers through a comprehensive investigation.

METHODS

Target genes associated with butein were sourced from SwissTargetPrediction, CTD, BindingDB and TargetNet. Gene expression data from the GSE38026 dataset and the single-cell dataset (GSE222300) were retrieved from the Gene Expression Omnibus database. The activation of disease-related pathways was assessed using Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and differential gene analysis. Disease-associated genes were identified through differential analysis and weighted gene co-expression network analysis (WGCNA). The protein-protein interaction network was utilized to pinpoint potential drug targets. Molecular complex detection (MCODE) analysis was employed to uncover relevant genes influenced by butein within key subgroup networks. Machine learning techniques were applied for the screening of potential biomarkers, with receiver operating characteristic curves used to evaluate their clinical significance. Single-cell analysis was conducted to assess the pharmacological targets of butein in CRC, with validation performed using the external dataset GSE40967.

RESULTS

A total of 232 target genes for butein were identified. Functional enrichment analysis revealed significant enrichment of signaling pathways, including mitogen-activated protein kinase, JAK-STAT and NF-κB, among these genes. Differential analysis, in conjunction with WGCNA, yielded 520 disease-related genes. Subsequently, a disease-drug-gene network consisting of 727 targets was established, and a subnetwork containing 56 crucial genes was extracted. Important pathways such as the FoxO signaling pathway exhibited significant enrichment within these key genes. Machine learning applied to the 56 important genes led to the identification of a potential biomarker, UBE2C. Receiver operating characteristic analysis demonstrated the excellent clinical predictive utility of UBE2C. Single-cell analysis suggested that butein's therapeutic effects might be linked to its influence on epithelial and T cells, with UBE2C expression associated with these cell types. Validation using the external dataset GSE40967 further confirmed the exceptional clinical predictive capability of UBE2C.

CONCLUSION

This study combines network pharmacology with single-cell analysis to unravel the mechanisms underlying butein's effects in CRC. Notably, UBE2C emerged as a promising biomarker with superior clinical efficacy. These research findings contribute significantly to our understanding of specific molecular mechanisms, potentially shaping future clinical practices.

Phenotypic shifts induced by environmental pre-stressors modify antibiotic resistance in Staphylococcus aureus

Introduction

Escalating prevalence of antibiotic resistance in Staphylococcus aureus has necessitated urgent exploration into the fundamental mechanisms underlying antibiotic resistance emergence, particularly in relation to its interaction with environmental stressors. This study aimed to investigate the effects of environmental stressors prior to antibiotic exposure on the antibiotic resistance of S. aureus.

Methods

We used Raman spectroscopy and flow cytometry to measure prior stress-induced phenotypic alterations of S. aureus, and identified the association between phenotypic shifts and the antibiotic resistance.

Results

The results revealed a multifaceted relationship between stressors and the development of antibiotic resistance. The stressors effectuate distinct phenotypic diversifications and subsequently amplify these phenotypic alterations following antibiotic treatments, contingent upon the specific mode of action; these phenotypic shifts in turn promote the development of antibiotic resistance in S. aureus. This study's findings demonstrated that the presence of pre-stress conditions triggered an augmentation of resistance to vancomycin (VAN), while concurrently attenuating resistance to norfloxacin. Marked shifts in Raman peaks associated with lipids and nucleic acids demonstrated correlations with elevated survival rates following VAN treatment.

Conclusion

Consequently, these observations indicate that pre-stress conditions "prime" bacterial cells for differential responses to antibiotics and bear significant implications for formulating clinical therapeutic strategies.

ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods

Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.

Zwitterionic microgel preservation platform for circulating tumor cells in whole blood specimen

The immediate processing of whole blood specimen is required in circulating tumor cell-based liquid biopsy. Reliable blood specimen stabilization towards preserving circulating tumor cells can enable more extensive geographic sharing for precise rare-cell technology, but remains challenging due to the fragility and rarity of circulating tumor cells. Herein, we establish a zwitterionic magnetic microgel platform to stabilize whole blood specimen for long-term hypothermic preservation of model circulating tumor cells. We show in a cohort study of 20 cancer patients that blood samples can be preserved for up to 7 days without compromising circulating tumor cell viability and RNA integrity, thereby doubling the viable preservation duration. We demonstrate that the 7-day microgel-preserved blood specimen is able to reliably detect cancer-specific transcripts, similar to fresh blood specimens, while there are up/down expression regulation of 1243 genes in model circulating tumor cells that are preserved by commercial protectant. Mechanistically, we find that the zwitterionic microgel assembly counters the cold-induced excessive reactive oxygen species and platelet activation, as well as extracellular matrix loss-induced cell anoikis, to prevent circulating tumor cell loss in the whole blood sample. The present work could prove useful for the development of blood-based noninvasive diagnostics.

Loss of SYNCRIP unleashes APOBEC-driven mutagenesis, tumor heterogeneity, and AR-targeted therapy resistance in prostate cancer

Tumor mutational burden and heterogeneity has been suggested to fuel resistance to many targeted therapies. The cytosine deaminase APOBEC proteins have been implicated in the mutational signatures of more than 70% of human cancers. However, the mechanism underlying how cancer cells hijack the APOBEC mediated mutagenesis machinery to promote tumor heterogeneity, and thereby foster therapy resistance remains unclear. We identify SYNCRIP as an endogenous molecular brake which suppresses APOBEC-driven mutagenesis in prostate cancer (PCa). Overactivated APOBEC3B, in SYNCRIP-deficient PCa cells, is a key mutator, representing the molecular source of driver mutations in some frequently mutated genes in PCa, including FOXA1, EP300. Functional screening identifies eight crucial drivers for androgen receptor (AR)-targeted therapy resistance in PCa that are mutated by APOBEC3B: BRD7, CBX8, EP300, FOXA1, HDAC5, HSF4, STAT3, and AR. These results uncover a cell-intrinsic mechanism that unleashes APOBEC-driven mutagenesis, which plays a significant role in conferring AR-targeted therapy resistance in PCa.

Characteristics and molecular mechanism of drug-tolerant cells in cancer: a review

Drug resistance in tumours has seriously hindered the therapeutic effect. Tumour drug resistance is divided into primary resistance and acquired resistance, and the recent study has found that a significant proportion of cancer cells can acquire stable drug resistance from scratch. This group of cells first enters the drug tolerance state (DT state) under drug pressure, and gradually acquires stable drug resistance through adaptive mutations in this state. Although the specific mechanisms underlying the formation of drug tolerant cells (DTCs) remain unclear, various proteins and signalling pathways have been identified as being involved in the formation of DTCs. In the current review, we summarize the characteristics, molecular mechanisms and therapeutic strategies of DTCs in detail.

"SMART" cytology: The next generation cytology for precision diagnosis

Cytology plays an important role in diagnosing and managing human diseases, especially cancer, as it is often a simple, low cost yet effective, and non-invasive or minimally invasive diagnostic tool. However, traditional morphology-based cytology practice has limitations, especially in the era of precision diagnosis. Recently there have been tremendous efforts devoted to apply computational tools and to perform molecular analysis on cytological samples for a variety of clinical purposes. Now is probably the appropriate juncture to integrate morphology, machine learning, and molecular analysis together and transform cytology from a morphology-driven practice to the next level - "SMART" Cytology. In this article we will provide a rather brief review of the relevant works for computational analysis on cytology samples, focusing on single-cell-based multiplex quantitative analysis of biomarkers, and introduce the conceptual framework of "SMART (Single cell, Multiplex, AI-driven, and Real Time)" Cytology.

Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology

Paclitaxel is an herbal active ingredient used in clinical practice that shows anti-tumor effects. However, its biological activity, mechanism, and cancer cell-killing effects remain unknown. Information on the chemical gene interactions of paclitaxel was obtained from the Comparative Toxicogenomics Database, SwishTargetPrediction, Binding DB, and TargetNet databases. Gene expression data were obtained from the GSE4290 dataset. Differential gene analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology analyses were performed. Gene set enrichment analysis was performed to evaluate disease pathway activation; weighted gene co-expression network analysis with diff analysis was used to identify disease-associated genes, analyze differential genes, and identify drug targets via protein-protein interactions. The Molecular Complex Detection (MCODE) analysis of critical subgroup networks was conducted to identify essential genes affected by paclitaxel, assess crucial cluster gene expression differences in glioma versus standard samples, and perform receiver operator characteristic mapping. To evaluate the pharmacological targets and signaling pathways of paclitaxel in glioblastoma, the single-cell GSE148196 dataset was acquired from the Gene Expression Omnibus database and preprocessed using Seurat software. Based on the single-cell RNA-sequencing dataset, 24 cell clusters were identified, along with marker genes for the two different cell types in each cluster. Correlation analysis revealed that the mechanism of paclitaxel treatment involves effects on neurons. Paclitaxel may affect glioblastoma by improving glucose metabolism and processes involved in modulating immune function in the body.

Influence of single-cell RNA sequencing data integration on the performance of differential gene expression analysis

Large-scale comprehensive single-cell experiments are often resource-intensive and require the involvement of many laboratories and/or taking measurements at various times. This inevitably leads to batch effects, and systematic variations in the data that might occur due to different technology platforms, reagent lots, or handling personnel. Such technical differences confound biological variations of interest and need to be corrected during the data integration process. Data integration is a challenging task due to the overlapping of biological and technical factors, which makes it difficult to distinguish their individual contribution to the overall observed effect. Moreover, the choice of integration method may impact the downstream analyses, including searching for differentially expressed genes. From the existing data integration methods, we selected only those that return the full expression matrix. We evaluated six methods in terms of their influence on the performance of differential gene expression analysis in two single-cell datasets with the same biological study design that differ only in the way the measurement was done: one dataset manifests strong batch effects due to the measurements of each sample at a different time. Integrated data were visualized using the UMAP method. The evaluation was done both on individual gene level using parametric and non-parametric approaches for finding differentially expressed genes and on gene set level using gene set enrichment analysis. As an evaluation metric, we used two correlation coefficients, Pearson and Spearman, of the obtained test statistics between reference, test, and corrected studies. Visual comparison of UMAP plots highlighted ComBat-seq, limma, and MNN, which reduced batch effects and preserved differences between biological conditions. Most of the tested methods changed the data distribution after integration, which negatively impacts the use of parametric methods for the analysis. Two algorithms, MNN and Scanorama, gave very poor results in terms of differential analysis on gene and gene set levels. Finally, we highlight ComBat-seq as it led to the highest correlation of test statistics between reference and corrected dataset among others. Moreover, it does not distort the original distribution of gene expression data, so it can be used in all types of downstream analyses.

Gene expression based inference of cancer drug sensitivity

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.

Predicting treatment response in cancer remains a highly complex task. Here, the authors develop Precily, a deep neural network framework to predict treatment response in cancer by considering gene expression, pathway activity estimates and drug features, and test this method in multiple datasets and preclinical models.

Dissecting Molecular Heterogeneity of Circulating Tumor Cells (CTCs) from Metastatic Breast Cancer Patients through Copy Number Aberration (CNA) and Single Nucleotide Variant (SNV) Single Cell Analysis

Circulating tumor cells' (CTCs) heterogeneity contributes to counteract their introduction in clinical practice. Through single-cell sequencing we aim at exploring CTC heterogeneity in metastatic breast cancer (MBC) patients. Single CTCs were isolated using DEPArray NxT. After whole genome amplification, libraries were prepared for copy number aberration (CNA) and single nucleotide variant (SNV) analysis and sequenced using Ion GeneStudio S5 and Illumina MiSeq, respectively. CTCs demonstrate distinctive mutational signatures but retain molecular traces of their common origin. CNA profiling identifies frequent aberrations involving critical genes in pathogenesis: gains of 1q (CCND1) and 11q (WNT3A), loss of 22q (CHEK2). The longitudinal single-CTC analysis allows tracking of clonal selection and the emergence of resistance-associated aberrations, such as gain of a region in 12q (CDK4). A group composed of CTCs from different patients sharing common traits emerges. Further analyses identify losses of 15q and enrichment of terms associated with pseudopodium formation as frequent and exclusive events. CTCs from MBC patients are heterogeneous, especially concerning their mutational status. The single-cell analysis allows the identification of aberrations associated with resistance, and is a candidate tool to better address treatment strategy. The translational significance of the group populated by similar CTCs should be elucidated.

Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity

Single-cell RNA-sequencing (scRNA-seq) technologies enable the measurements of gene expressions in individual cells, which is helpful for exploring cancer heterogeneity and precision medicine. However, various technical noises lead to false zero values (missing gene expression values) in scRNA-seq data, termed as dropout events. These zero values complicate the analysis of cell patterns, which affects the high-precision analysis of intra-tumor heterogeneity. Recovering missing gene expression values is still a major obstacle in the scRNA-seq data analysis. In this study, taking the cell heterogeneity into consideration, we develop a novel method, called single cell Gauss–Newton Gene expression Imputation (scGNGI), to impute the scRNA-seq expression matrices by using a low-rank matrix completion. The obtained experimental results on the simulated datasets and real scRNA-seq datasets show that scGNGI can more effectively impute the missing values for scRNA-seq gene expression and improve the down-stream analysis compared to other state-of-the-art methods. Moreover, we show that the proposed method can better preserve gene expression variability among cells. Overall, this study helps explore the complex biological system and precision medicine in scRNA-seq data.

Early Steps of Resistance to Targeted Therapies in Non-Small-Cell Lung Cancer

Simple Summary

Patients with lung cancer benefit from more effective treatments, such as targeted therapies, and the overall survival has increased in the past decade. However, the efficacy of targeted therapies is limited due to the emergence of resistance. Growing evidence suggests that resistances may arise from a small population of drug-tolerant persister (DTP) cells. Understanding the mechanisms underlying DTP survival is therefore crucial to develop therapeutic strategies to prevent the development of resistance. Herein, we propose an overview of the current scientific knowledge about the characterisation of DTP, and summarise the new therapeutic strategies that are tested to target these cells.

Abstract

Lung cancer is the leading cause of cancer-related deaths among men and women worldwide. Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are effective therapies for advanced non-small-cell lung cancer (NSCLC) patients harbouring EGFR-activating mutations, but are not curative due to the inevitable emergence of resistances. Recent in vitro studies suggest that resistance to EGFR-TKI may arise from a small population of drug-tolerant persister cells (DTP) through non-genetic reprogramming, by entering a reversible slow-to-non-proliferative state, before developing genetically derived resistances. Deciphering the molecular mechanisms governing the dynamics of the drug-tolerant state is therefore a priority to provide sustainable therapeutic solutions for patients. An increasing number of molecular mechanisms underlying DTP survival are being described, such as chromatin and epigenetic remodelling, the reactivation of anti-apoptotic/survival pathways, metabolic reprogramming, and interactions with their micro-environment. Here, we review and discuss the existing proposed mechanisms involved in the DTP state. We describe their biological features, molecular mechanisms of tolerance, and the therapeutic strategies that are tested to target the DTP.

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis

Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

Can aggressive cancers be identified by the "aggressiveness" of their chromatin?

Phenotypic plasticity is a crucial feature of aggressive cancer, providing the means for cancer progression. Stochastic changes in tumor cell transcriptional programs increase the chances of survival under any condition. I hypothesize that unstable chromatin permits stochastic transitions between transcriptional programs in aggressive cancers and supports non-genetic heterogeneity of tumor cells as a basis for their adaptability. I present a mechanistic model for unstable chromatin which includes destabilized nucleosomes, mobile chromatin fibers and random enhancer-promoter contacts, resulting in stochastic transcription. I suggest potential markers for "unsettled" chromatin in tumors associated with poor prognosis. Although many of the characteristics of unstable chromatin have been described, they were mostly used to explain changes in the transcription of individual genes. I discuss approaches to evaluate the role of unstable chromatin in non-genetic tumor cell heterogeneity and suggest using the degree of chromatin instability and transcriptional noise in tumor cells to predict cancer aggressiveness.

Single-Cell Image-Based Analysis Reveals Chromatin Changes during the Acquisition of Tamoxifen Drug Resistance

Cancer drug resistance is the leading cause of cancer related deaths. The development of drug resistance can be partially contributed to tumor heterogeneity and epigenetic plasticity. However, the detailed molecular mechanism underlying epigenetic modulated drug resistance remains elusive. In this work, we systematically analyzed epigenetic changes in tamoxifen (Tam) responsive and resistant breast cancer cell line MCF7, and adopted a data-driven approach to identify key epigenetic features distinguishing between these two cell types. Significantly, we revealed that DNA methylation and H3K9me3 marks that constitute the heterochromatin are distinctively different between Tam-resistant and -responsive cells. We then performed time-lapse imaging of 5mC and H3K9me3 features using engineered probes. After Tam treatment, we observed a slow transition of MCF7 cells from a drug-responsive to -resistant population based on DNA methylation features. A similar trend was not observed using H3K9me3 probes. Collectively, our results suggest that DNA methylation changes partake in the establishment of Tam-resistant breast cancer cell lines. Instead of global changes in the DNA methylation level, the distribution of DNA methylation features inside the nucleus can be one of the drivers that facilitates the establishment of a drug resistant phenotype in MCF7.

Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues

Single-cell RNA sequencing (RNA-seq) techniques can perform analysis of transcriptome at the single-cell level and possess an unprecedented potential for exploring signatures involved in tumor development and progression. These techniques can perform sequence analysis of transcripts with a better resolution that could increase understanding of the cellular diversity found in the tumor microenvironment and how the cells interact with each other in complex heterogeneous cancerous tissues. Identifying the changes occurring in the genome and transcriptome in the spatial context is considered to increase knowledge of molecular factors fueling cancers. It may help develop better monitoring strategies and innovative approaches for cancer treatment. Recently, there has been a growing trend in the integration of RNA-seq techniques with contemporary omics technologies to study the tumor microenvironment. There has been a realization that this area of research has a huge scope of application in translational research. This review article presents an overview of various types of single-cell RNA-seq techniques used currently for analysis of cancer tissues, their pros and cons in bulk profiling of transcriptome, and recent advances in the techniques in exploring heterogeneity of various types of cancer tissues. Furthermore, we have highlighted the integration of single-cell RNA-seq techniques with other omics technologies for analysis of transcriptome in their spatial context, which is considered to revolutionize the understanding of tumor microenvironment.

Transcriptome analysis method based on differential distribution evaluation

Identifying differential genes over conditions provides insights into the mechanisms of biological processes and disease progression. Here we present an approach, the Kullback-Leibler divergence-based differential distribution (klDD), which provides a flexible framework for quantifying changes in higher-order statistical information of genes including mean and variance/covariation. The method can well detect subtle differences in gene expression distributions in contrast to mean or variance shifts of the existing methods. In addition to effectively identifying informational genes in terms of differential distribution, klDD can be directly applied to cancer subtyping, single-cell clustering and disease early-warning detection, which were all validated by various benchmark datasets.

Pump-free microfluidic magnetic levitation approach for density-based cell characterization

Magnetic levitation (MagLev) provides a simple but promising method for density-based analysis and detection down to the individual cell level. However, each existing MagLev configuration for the single-cell density measurement, mainly consisting of a capillary (∼50 mm) placed between two magnets, yields a fairly low sample utilization because of no knowledge about the sample cells in the regions other than the limited microscope vision. Moreover, the quantitative analysis may be affected due to the unclearly defined measurement area, which is specifically associated with the uneven magnetization of magnets, cell size, degree of aggregation. In this work, we explore a pump-free microfluidic magnetic levitation approach for density-based cell characterization, enabling sensitive and effective cellular density measurement on small sample volumes. The microfluidic MagLev comprises a pump-free microfluidic chip placed between two ring magnets with like poles facing. With no external pumps, connectors or control facility, much smaller amounts of fluids (∼4 μL) could be driven automatically in the entire microchannel in 16 s. Based on the pump-free mechanism, unique density signatures of cells from different lineages (ARPE-19, HCT116, HeLa, HT1080, Huh7) are characterized by monitoring the levitation profiles. Furthermore, variation in density of A549 lung cancer cells subjected to a drug treatment are observed in our platform, allowing evaluation of the efficacy of the drug treatment at the individual cell level. Thereby, the proposed pump-free microfluidic MagLev platform, a low-cost, fully automatic and portable design for label-free density-based cell characterization, provides a universal detection tool that operates efficiently within small-volume environments.

Epigenetic Alterations and Mechanisms That Drive Resistance to Targeted Cancer Therapies

Cancer is a complex disease and cancer cells typically harbor multiple genetic and epigenetic alterations. Large-scale sequencing of patient-derived cancer samples has identified several druggable driver oncogenes. Many of these oncogenes can be pharmacologically targeted to provide effective therapies for breast cancer, leukemia, lung cancer, melanoma, lymphoma, and other cancer types. Initial responses to these agents can be robust in many cancer types and some patients with cancer experience sustained tumor inhibition. However, resistance to these targeted therapeutics frequently emerges, either from intrinsic or acquired mechanisms, posing a major clinical hurdle for effective treatment. Several resistance mechanisms, both cell autonomous and cell nonautonomous, have been identified in different cancer types. Here we describe how alterations of the transcriptome, transcription factors, DNA, and chromatin regulatory proteins confer resistance to targeted therapeutic agents. We also elaborate on how these studies have identified underlying epigenetic factors that drive drug resistance and oncogenic pathways, with direct implications for the prevention and treatment of drug-resistant cancer.

From bulk, single-cell to spatial RNA sequencing

RNA sequencing (RNAseq) can reveal gene fusions, splicing variants, mutations/indels in addition to differential gene expression, thus providing a more complete genetic picture than DNA sequencing. This most widely used technology in genomics tool box has evolved from classic bulk RNA sequencing (RNAseq), popular single cell RNA sequencing (scRNAseq) to newly emerged spatial RNA sequencing (spRNAseq). Bulk RNAseq studies average global gene expression, scRNAseq investigates single cell RNA biology up to 20,000 individual cells simultaneously, while spRNAseq has ability to dissect RNA activities spatially, representing next generation of RNA sequencing. This article highlights these technologies, characteristic features and suitable applications in precision oncology.

Emerging strategies for the improvement of chemotherapy in bladder cancer: Current knowledge and future perspectives

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The response of chemotherapy and prognosis in bladder cancer is unsatisfied.

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Immunotherapy, targeted therapy, and ADC improve the efficacy of chemotherapy.

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Emerging targets in cancer cells and TME spawned novel preclinical agents.

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Novel drug delivery, such as nanotechnology, enhances effects of chemotherapeutics.

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The organoid and PDX model are promising to screen and evaluate the target therapy.

Background

Chemotherapy is a first-line treatment for advanced and metastatic bladder cancer, but the unsatisfactory objective response rate to this treatment yields poor 5-year patient survival. Only PD-1/PD-L1-based immune checkpoint inhibitors, FGFR3 inhibitors and antibody-drug conjugates are approved by the FDA to be used in bladder cancer, mainly for platinum-refractory or platinum-ineligible locally advanced or metastatic urothelial carcinoma. Emerging studies indicate that the combination of targeted therapy and chemotherapy shows better efficacy than targeted therapy or chemotherapy alone. Newly identified targets in cancer cells and various functions of the tumour microenvironment have spawned novel agents and regimens, which give impetus to sensitizing chemotherapy in the bladder cancer setting.

Aim of Review

This review aims to present the current evidence for potentiating the efficacy of chemotherapy in bladder cancer. We focus on combining chemotherapy with other treatments as follows: targeted therapy, including immunotherapy and antibody-drug conjugates in clinic; novel targeted drugs and nanoparticles in preclinical models and potential targets that may contribute to chemosensitivity in future clinical practice. The prospect of precision therapy is also discussed in bladder cancer.

Key Scientific Concepts of Review

Combining chemotherapy drugs with immune checkpoint inhibitors, antibody-drug conjugates and VEGF inhibitors potentially elevates the response rate and survival. Novel targets, including cancer stem cells, DNA damage repair, antiapoptosis, drug metabolism and the tumour microenvironment, contribute to chemosensitization. Gene alteration-based drug selection and patient-derived xenograft- and organoid-based drug validation are the future for precision therapy.

Computer vision reveals hidden variables underlying NF-κB activation in single cells

Individual cells are heterogeneous when responding to environmental cues. Under an external signal, certain cells activate gene regulatory pathways, while others completely ignore that signal. Mechanisms underlying cellular heterogeneity are often inaccessible because experiments needed to study molecular states destroy the very states that we need to examine. Here, we developed an image-based support vector machine learning model to uncover variables controlling activation of the immune pathway nuclear factor κB (NF-κB). Computer vision analysis predicts the identity of cells that will respond to cytokine stimulation and shows that activation is predetermined by minute amounts of “leaky” NF-κB (p65:p50) localization to the nucleus. Mechanistic modeling revealed that the ratio of NF-κB to inhibitor of NF-κB predetermines leakiness and activation probability of cells. While cells transition between molecular states, they maintain their overall probabilities for NF-κB activation. Our results demonstrate how computer vision can find mechanisms behind heterogeneous single-cell activation under proinflammatory stimuli.

Multiplex gene quantification as digital markers for extremely rapid evaluation of chemo-drug sensitivity

Current administrations for precision drug uses are limited in evaluation speed. Here, we propose the use of multiplex gene-based digital markers for the extremely rapid personalized prediction of individual sensitivity to cancer drugs. We first screen the transcriptional profiles by applying two to three gene filters and scoring genes by their impact on drug sensitivity and finalize the gene lists by K-nearest neighbors cross-validation. The digital markers are cancer type dependent, are composed of tens to hundreds of gene expressions, and are rapidly quantified by reverse transcription quantitative real-time PCR (qRT-PCR) within 1–3 h after tumor sampling. The area under the receiver operating characteristic curve reached 0.88 when testing the performance of digital markers on organoids derived from colorectal cancer patient tumors. The algorithm and corresponding graphic user interface were developed to demonstrate the promise of digital markers for extremely rapid drug recommendation.

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Non-targeted multiplex genes are screened as digital markers for drug sensitivity

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Transcription level cohort of 10s to 100s genes predicts drug sensitivity

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Digital markers are quantified using qRT-PCR within 1–3 h

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Digital markers guide extremely rapid chemo-drug uses after patient hospitalization

In clinical cancer medicine, many patients require immediate chemotherapy after hospitalization. Current administrations for precision drug uses are limited in evaluation speed, including genomic sequencing and tumor organoid evaluation. An extremely rapid evaluation protocol is in high demand to realize drug recommendation within a few hours after tumor sampling. In this work, we have proposed an approach for extremely rapid and personalized drug recommendation.

Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression

The prognosis of most patients with AML is poor due to frequent disease relapse. The cause of relapse is thought to be from the persistence of leukemia initiating cells (LIC's) following treatment. Here we assessed RNA based changes in LICs from matched patient diagnosis and relapse samples using single-cell RNA sequencing. Previous studies on AML progression have focused on genetic changes at the DNA mutation level mostly in bulk AML cells and demonstrated the existence of DNA clonal evolution. Here we identified in LICs that the phenomenon of RNA clonal evolution occurs during AML progression. Despite the presence of vast transcriptional heterogeneity at the single cell level, pathway analysis identified common signaling networks involving metabolism, apoptosis and chemokine signaling that evolved during AML progression and become a signature of relapse samples. A subset of this gene signature was validated at the protein level in LICs by flow cytometry from an independent AML cohort and functional studies were performed to demonstrate co-targeting BCL2 and CXCR4 signaling may help overcome therapeutic challenges with AML heterogeneity. It is hoped this work will facilitate a greater understanding of AML relapse leading to improved prognostic biomarkers and therapeutic strategies to target LIC's.

IRS1 phosphorylation underlies the non-stochastic probability of cancer cells to persist during EGFR inhibition therapy

Stochastic transition of cancer cells between drug-sensitive and drug-tolerant persister phenotypes has been proposed to play a key role in non-genetic resistance to therapy. Yet, we show here that cancer cells actually possess a highly stable inherited chance to persist (CTP) during therapy. This CTP is non-stochastic, determined pre-treatment and has a unimodal distribution ranging from 0 to almost 100%. Notably, CTP is drug specific. We found that differential serine/threonine phosphorylation of the insulin receptor substrate 1 (IRS1) protein determines the CTP of lung and of head and neck cancer cells under epidermal growth factor receptor inhibition, both in vitro and in vivo. Indeed, the first-in-class IRS1 inhibitor NT219 was highly synergistic with anti-epidermal growth factor receptor therapy across multiple in vitro and in vivo models. Elucidation of drug-specific mechanisms that determine the degree and stability of cellular CTP may establish a framework for the elimination of cancer persisters, using new rationally designed drug combinations.

Cellular origins and lineage relationships of the intestinal epithelium

Knowledge of the development and hierarchical organization of tissues is key to understanding how they are perturbed in injury and disease, as well as how they may be therapeutically manipulated to restore homeostasis. The rapidly regenerating intestinal epithelium harbors diverse cell types and their lineage relationships have been studied using numerous approaches, from classical label-retaining and genetic lineage tracing methods to novel transcriptome-based annotations. Here, we describe the developmental trajectories that dictate differentiation and lineage specification in the intestinal epithelium. We focus on the most recent single-cell RNA-sequencing (scRNA-seq)-based strategies for understanding intestinal epithelial cell lineage relationships, underscoring how they have refined our view of the development of this tissue and highlighting their advantages and limitations. We emphasize how these technologies have been applied to understand the dynamics of intestinal epithelial cells in homeostatic and injury-induced regeneration models.

Cell-intrinsic mechanisms of drug tolerance to systemic therapies in cancer

In cancer patients with metastatic disease, the rate of complete tumor response to systemic therapies is low, and residual lesions persist in the majority of patients due to early molecular adaptation in cancer cells. A growing body of evidence suggests that a subpopulation of drug-tolerant « persister » cells - a reversible phenotype characterized by reduced drug sensitivity and decreased cell proliferation - maintains residual disease and may serve as a reservoir for resistant phenotypes. The survival of these residual tumor cells can be caused by reactivation of specific signaling pathways, phenotypic plasticity (i.e., transdifferentiation), epigenetic or metabolic reprogramming, downregulation of apoptosis as well as transcriptional remodeling. In this review, we discuss the molecular mechanisms that enable adaptive survival in drug-tolerant cells. We describe the main characteristics and dynamic nature of this persistent state, and highlight the current therapeutic strategies that may be used to interfere with the establishment of drug-tolerant cells, as an alternative to improve objective response to systemic therapies and delay the emergence of resistance to improve long-term survival.

SpaceM reveals metabolic states of single cells

A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

Cancer stem cells in TNBC

Triple-negative breast cancer (TNBC) is a broad collection of breast cancer that tests negative for estrogen receptors (ER), progesterone receptors (PR), and excess human epidermal growth factor receptor 2 (HER2) protein. TNBC is considered to have poorer prognosis than other types of breast cancer because of a lack of effective therapeutic targets. The success of precision cancer therapies relies on the clarification of key molecular mechanisms that drive tumor growth and metastasis; however, TNBC is highly heterogeneous in terms of their cellular lineage composition and the molecular nature within each individual case. In particular, the rare and sometimes slow cycling cancer stem cells (CSCs) can provide effective means for TNBC to resist various treatments. Single cell analysis technologies, including single-cell RNA-seq (scRNA-seq) and proteomics, provide an avenue to unravel patient-level intratumoral heterogeneity by identifying CSCs populations, CSC biomarkers and the range of tumor microenvironment cellular constituents that contribute to tumor growth. This review discusses the emerging evidence for the role of CSCs in driving TNBC incidence and the therapeutic implications in manipulating molecular signaling against this rare cell population for the control of this deadly disease.

Evolution of core archetypal phenotypes in progressive high grade serous ovarian cancer

The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells following chemotherapy is only partially understood. To understand the selection of factors driving heterogeneity before and through adaptation to treatment, we profile single-cell RNA-sequencing (scRNA-seq) transcriptomes of HGSOC tumors collected longitudinally during therapy. We analyze scRNA-seq data from two independent patient cohorts to reveal that HGSOC is driven by three archetypal phenotypes, defined as oncogenic states that describe the majority of the transcriptome variation. Using a multi-task learning approach to identify the biological tasks of each archetype, we identify metabolism and proliferation, cellular defense response, and DNA repair signaling as consistent cell states found across patients. Our analysis demonstrates a shift in favor of the metabolism and proliferation archetype versus cellular defense response archetype in cancer cells that received multiple lines of treatment. While archetypes are not consistently associated with specific whole-genome driver mutations, they are closely associated with subclonal populations at the single-cell level, indicating that subclones within a tumor often specialize in unique biological tasks. Our study reveals the core archetypes found in progressive HGSOC and shows consistent enrichment of subclones with the metabolism and proliferation archetype as resistance is acquired to multiple lines of therapy.

What are the applications of single-cell RNA sequencing in cancer research: a systematic review

Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.

High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology

The complex interaction of cells with biomaterials (i.e., materiobiology) plays an increasingly pivotal role in the development of novel implants, biomedical devices, and tissue engineering scaffolds to treat diseases, aid in the restoration of bodily functions, construct healthy tissues, or regenerate diseased ones. However, the conventional approaches are incapable of screening the huge amount of potential material parameter combinations to identify the optimal cell responses and involve a combination of serendipity and many series of trial-and-error experiments. For advanced tissue engineering and regenerative medicine, highly efficient and complex bioanalysis platforms are expected to explore the complex interaction of cells with biomaterials using combinatorial approaches that offer desired complex microenvironments during healing, development, and homeostasis. In this review, we first introduce materiobiology and its high-throughput screening (HTS). Then we present an in-depth of the recent progress of 2D/3D HTS platforms (i.e., gradient and microarray) in the principle, preparation, screening for materiobiology, and combination with other advanced technologies. The Compendium for Biomaterial Transcriptomics and high content imaging, computational simulations, and their translation toward commercial and clinical uses are highlighted. In the final section, current challenges and future perspectives are discussed. High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials.

Deubiquitinating enzyme inhibitor alleviates cyclin A1-mediated proteasome inhibitor tolerance in mixed-lineage leukemia

Drug resistance is a significant obstacle to effective cancer treatment. Drug resistance develops from initially reversible drug-tolerant cancer cells, which offer therapeutic opportunities to impede cancer relapse. The mechanisms of resistance to proteasome inhibitor (PI) therapy have been investigated intensively, however the ways by which drug-tolerant cancer cells orchestrate their adaptive responses to drug challenges remain largely unknown. Here, we demonstrated that cyclin A1 suppression elicited the development of transient PI tolerance in mixed-lineage leukemia (MLL) cells. This adaptive process involved reversible downregulation of cyclin A1, which promoted PI resistance through cell-cycle arrest. PI-tolerant MLL cells acquired cyclin A1 dependency, regulated directly by MLL protein. Loss of cyclin A1 function resulted in the emergence of drug tolerance, which was associated with patient relapse and reduced survival. Combination treatment with PI and deubiquitinating enzyme (DUB) inhibitors overcame this drug resistance by restoring cyclin A1 expression through chromatin crosstalk between histone H2B monoubiquitination and MLL-mediated histone H3 lysine 4 methylation. These results reveal the importance of cyclin A1-engaged cell-cycle regulation in PI resistance in MLL cells, and suggest that cell-cycle re-entry by DUB inhibitors may represent a promising epigenetic therapeutic strategy to prevent acquired drug resistance.

A Novel Method to Identify the Differences Between Two Single Cell Groups at Single Gene, Gene Pair, and Gene Module Levels

Single-cell sequencing technology can not only view the heterogeneity of cells from a molecular perspective, but also discover new cell types. Although there are many effective methods on dropout imputation, cell clustering, and lineage reconstruction based on single cell RNA sequencing (RNA-seq) data, there is no systemic pipeline on how to compare two single cell clusters at the molecular level. In the study, we present a novel pipeline on comparing two single cell clusters, including calling differential gene expression, coexpression network modules, and so on. The pipeline could reveal mechanisms behind the biological difference between cell clusters and cell types, and identify cell type specific molecular mechanisms. We applied the pipeline to two famous single-cell databases, Usoskin from mouse brain and Xin from human pancreas, which contained 622 and 1,600 cells, respectively, both of which were composed of four types of cells. As a result, we identified many significant differential genes, differential gene coexpression and network modules among the cell clusters, which confirmed that different cell clusters might perform different functions.

Single-cell sequencing in translational cancer research and challenges to meet clinical diagnostic needs

The ability to capture alterations in the genome or transcriptome by next-generation sequencing has provided critical insight into molecular changes and programs underlying cancer biology. With the rapid technological development in single-cell sequencing, it has become possible to study individual cells at the transcriptional, genetic, epigenetic, and protein level. Using single-cell analysis, an increased resolution of fundamental processes underlying cancer development is obtained, providing comprehensive insights otherwise lost by sequencing of entire (bulk) samples, in which molecular signatures of individual cells are averaged across the entire cell population. Here, we provide a concise overview on the application of single-cell analysis of different modalities within cancer research by highlighting key articles of their respective fields. We furthermore examine the potential of existing technologies to meet clinical diagnostic needs and discuss current challenges associated with this translation.

Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression

Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells.

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Insulin signaling is heterogeneous between cells in the same population

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The temporal response of signaling components within a cell is highly reproducible

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Upstream responses (Akt) can only partially predict downstream response (GLUT4)

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Protein expression variance is a driver of intercellular signaling heterogeneity

Single-Cell Sequencing and Organoids: A Powerful Combination for Modelling Organ Development and Diseases

The development and function of a particular organ and the pathogenesis of various diseases remain intimately linked to the features of each cell type in the organ. Conventional messenger RNA- or protein-based methodologies often fail to elucidate the contribution of rare cell types, including some subpopulations of stem cells, short-lived progenitors and circulating tumour cells, thus hampering their applications in studies regarding organ development and diseases. The scRNA-seq technique represents a new approach for determining gene expression variability at the single-cell level. Organoids are new preclinical models that recapitulate complete or partial features of their original organ and are thought to be superior to cell models in mimicking the sophisticated spatiotemporal processes of the development and regeneration and diseases. In this review, we highlight recent advances in the field of scRNA-seq, organoids and their current applications and summarize the advantages of using a combination of scRNA-seq and organoid technology to model diseases and organ development.

Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments

High-throughput sequencing (HTS) has revolutionized researchers' ability to study the human transcriptome, particularly as it relates to cancer. Recently, HTS technology has advanced to the point where now one is able to sequence individual cells (i.e., "single-cell sequencing"). Prior to single-cell sequencing technology, HTS would be completed on RNA extracted from a tissue sample consisting of multiple cell types (i.e., "bulk sequencing"). In this chapter, we review the various bioinformatics and statistical methods used in the processing, quality control, and analysis of bulk and single-cell RNA sequencing methods. Additionally, we discuss how these methods are also being used to study tumor heterogeneity.

ColorCells: a database of expression, classification and functions of lncRNAs in single cells

Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs' functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.

Modulating proteasome inhibitor tolerance in multiple myeloma: an alternative strategy to reverse inevitable resistance

Background

Resistance to proteasome inhibitors (PIs) is a major obstacle to the successful treatment of multiple myeloma (MM). Many mechanisms have been proposed for PI resistance; however, our mechanistic understanding of how PI resistance is inevitably acquired and reversed remains incomplete.

Methods

MM patients after bortezomib relapse, MM cell lines and mouse models were used to generate matched resistant and reversed cells. RNA sequencing and bioinformatics analyses were employed to assess dysregulated epigenetic regulators. In vitro and in vivo procedures were used to characterise PI-tolerant cells and therapeutic efficacy.

Results

Upon PI treatment, MM cells enter a slow-cycling and reversible drug-tolerant state. This reversible phenotype is associated with epigenetic plasticity, which involves tolerance rather than persistence in patients with relapsed MM. Combination treatment with histone deacetylase inhibitors and high-dosage intermittent therapy, as opposed to sustained PI monotherapy, can be more effective in treating MM by preventing the emergence of PI-tolerant cells. The therapeutic basis is the reversal of dysregulated epigenetic regulators in MM patients.

Conclusions

We propose an alternative non-mutational PI resistance mechanism that explains why PI relapse is inevitable and why patients regain sensitivity after a ‘drug holiday’. Our study also suggests strategies for epigenetic elimination of drug-tolerant cells.

Research and application of single-cell sequencing in tumor heterogeneity and drug resistance of circulating tumor cells

Malignant tumor is a largely harmful disease worldwide. The cure rate of malignant tumors increases with the continuous discovery of anti-tumor drugs and the optimisation of chemotherapy options. However, drug resistance of tumor cells remains a massive obstacle in the treatment of anti-tumor drugs. The heterogeneity of malignant tumors makes studying it further difficult for us. In recent years, using single-cell sequencing technology to study and analyse circulating tumor cells can avoid the interference of tumor heterogeneity and provide a new perspective for us to understand tumor drug resistance.

Force balancing ACT-IN the tumor microenvironment: Cytoskeletal modifications in cancer and stromal cells to promote malignancy

The tumor microenvironment is a complex milieu that dictates the growth, invasion, and metastasis of cancer cells. Both cancer and stromal cells in the tumor tissue encounter and adapt to a variety of extracellular factors, and subsequently contribute and drive the progression of the disease to more advanced stages. As the disease progresses, a small population of cancer cells becomes more invasive through a complex process known as epithelial-mesenchymal transition, and nearby stromal cells assume a carcinoma associated fibroblast phenotype characterized by enhanced migration, cell contractility, and matrix secretion with the ability to reorganize extracellular matrices. As cells transition into more malignant phenotypes their biophysical properties, controlled by the organization of cytoskeletal proteins, are altered. Actin and its associated proteins are essential modulators and facilitators of these changes. As the cells respond to the cues in the microenvironment, actin driven mechanical forces inside and outside the cells also evolve. Recent advances in biophysical techniques have enabled us to probe these actin driven changes in cancer and stromal cells and demarcate their role in driving changes in the microenvironment. Understanding the underlying biophysical mechanisms that drive cancer progression could provide critical insight on novel therapeutic approaches in the fight against cancer.