Recent progress in single-cell cancer genomics

Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE

The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer

Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.

SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics

The recent advance of single cell sequencing (scRNA-seq) technology such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) allows researchers to quantify cell surface protein abundance and RNA expression simultaneously at single cell resolution. Although CITE-seq and other similar technologies have gained enormous popularity, novel methods for analyzing this type of single cell multi-omics data are in urgent need. A limited number of available tools utilize data-driven approach, which may undermine the biological importance of surface protein data. In this study, we developed SECANT, a biology-guided SEmi-supervised method for Clustering, classification, and ANnoTation of single-cell multi-omics. SECANT is used to analyze CITE-seq data, or jointly analyze CITE-seq and scRNA-seq data. The novelties of SECANT include (1) using confident cell type label identified from surface protein data as guidance for cell clustering, (2) providing general annotation of confident cell types for each cell cluster, (3) utilizing cells with uncertain or missing cell type label to increase performance, and (4) accurate prediction of confident cell types for scRNA-seq data. Besides, as a model-based approach, SECANT can quantify the uncertainty of the results through easily interpretable posterior probability, and our framework can be potentially extended to handle other types of multi-omics data. We successfully demonstrated the validity and advantages of SECANT via simulation studies and analysis of public and in-house datasets from multiple tissues. We believe this new method will be complementary to existing tools for characterizing novel cell types and make new biological discoveries using single-cell multi-omics data.

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Summary: We propose quantitative methods to analyze cancer sc-RNAseq data: measures of intra-tumoral heterogeneity, characterization of a hierarchy of gene clusters, and alignment of gene cluster hierarchies from multiple datasets.

Pollock: fishing for cell states

Motivation

The use of single-cell methods is expanding at an ever-increasing rate. While there are established algorithms that address cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single-cell methods and analysis platforms, provides a set of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications.

Results

Pollock performs comparably to existing classification methods, while offering easily deployable pretrained classification models across a wide variety of tissue and data types. Additionally, it demonstrates utility in immune pan-cancer analysis.

Availability and implementation

Source code and documentation are available at https://github.com/ding-lab/pollock. Pretrained models and datasets are available for download at https://zenodo.org/record/5895221.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment

SIGNIFICANCE

There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology).

AIM

Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption.

APPROACH

We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations.

RESULTS

While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow.

CONCLUSIONS

The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.

Single Cell Self-Paced Clustering with Transcriptome Sequencing Data

Single cell RNA sequencing (scRNA-seq) allows researchers to explore tissue heterogeneity, distinguish unusual cell identities, and find novel cellular subtypes by providing transcriptome profiling for individual cells. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, the performance of existing single-cell clustering methods is extremely sensitive to the presence of noise data and outliers. Existing clustering algorithms can easily fall into local optimal solutions. There is still no consensus on the best performing method. To address this issue, we introduce a single cell self-paced clustering (scSPaC) method with F-norm based nonnegative matrix factorization (NMF) for scRNA-seq data and a sparse single cell self-paced clustering (sscSPaC) method with l21-norm based nonnegative matrix factorization for scRNA-seq data. We gradually add single cells from simple to complex to our model until all cells are selected. In this way, the influences of noisy data and outliers can be significantly reduced. The proposed method achieved the best performance on both simulation data and real scRNA-seq data. A case study about human clara cells and ependymal cells scRNA-seq data clustering shows that scSPaC is more advantageous near the clustering dividing line.

No Cell Left behind: Automated, Stochastic, Physics-Based Tracking of Every Cell in a Dense, Growing Colony

Motivation: Precise tracking of individual cells—especially tracking the family lineage, for example in a developing embryo—has widespread applications in biology and medicine. Due to significant noise in microscope images, existing methods have difficulty precisely tracking cell activities. These difficulties often require human intervention to resolve. Humans are helpful because our brain naturally and automatically builds a simulation “model” of any scene that we observe. Because we understand simple truths about the world—for example cells can move and divide, but they cannot instantaneously move vast distances—this model “in our heads” helps us to severely constrain the possible interpretations of what we see, allowing us to easily distinguish signal from noise, and track the motion of cells even in the presence of extreme levels of noise that would completely confound existing automated methods. Results: Here, we mimic the ability of the human brain by building an explicit computer simulation model of the scene. Our simulated cells are programmed to allow movement and cell division consistent with reality. At each video frame, we stochastically generate millions of nearby “Universes” and evolve them stochastically to the next frame. We then find and fit the best universes to reality by minimizing the residual between the real image frame and a synthetic image of the simulation. The rule-based simulation puts extremely stringent constraints on possible interpretations of the data, allowing our system to perform far better than existing methods even in the presense of extreme levels of image noise. We demonstrate the viability of this method by accurately tracking every cell in a colony that grows from 4 to over 300 individuals, doing about as well as a human can in the difficult task of tracking cell lineages.

Tackling Current Biomedical Challenges With Frontier Biofabrication and Organ-On-A-Chip Technologies

In the last decades, biomedical research has significantly boomed in the academia and industrial sectors, and it is expected to continue to grow at a rapid pace in the future. An in-depth analysis of such growth is not trivial, given the intrinsic multidisciplinary nature of biomedical research. Nevertheless, technological advances are among the main factors which have enabled such progress. In this review, we discuss the contribution of two state-of-the-art technologies-namely biofabrication and organ-on-a-chip-in a selection of biomedical research areas. We start by providing an overview of these technologies and their capacities in fabricating advanced in vitro tissue/organ models. We then analyze their impact on addressing a range of current biomedical challenges. Ultimately, we speculate about their future developments by integrating these technologies with other cutting-edge research fields such as artificial intelligence and big data analysis.

Population mutation properties of tumor evolution

Tumor growth patterns differ depending on the individual tumor, leading to various patterns of genetic heterogeneity across tumors. Despite their importance, the population mutation properties of tumor evolution have not been well studied, especially in terms of overall genetic heterogeneity. The current study aims to examine factors in tumor evolution influencing overall genetic heterogeneity. Extensive simulations of the representative evolutionary patterns of various tumors were conducted in the current study to determine the overall genetic characteristics of tumor evolution. The variations in cell birth/death rates and angiogenesis duration were examined based on the simplest growth pattern of tumors with avascular growth, angiogenesis, and vascular growth. To examine the impact of evolutionary tree structure, three-step linear evolution and branching evolution were investigated based on various subpopulation initiation times. The population size during the initial growth phase and the duration of angiogenesis are important factors affecting overall genetic heterogeneity. Furthermore, the shape of the evolutionary tree is crucial for defining the genetic heterogeneity of the extant tumor cell population, indicating the importance of the initiation timing of subpopulations. Branching evolution results in slightly greater genetic heterogeneity in terms of allelic distribution than in terms of the number of variants. The results of the current study reveal that the pattern of tumor evolution is critical for defining tumor genetic heterogeneity. These population genetic approaches are important for understanding the properties of tumor cell populations.

Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring

Statistical analysis of single cell RNA-sequencing (scRNA-seq) data is hindered by high levels of technical noise and inflated zero counts. One promising approach for addressing these challenges is gene set testing, or pathway analysis, which can mitigate sparsity and noise, and improve interpretation and power, by aggregating expression data to the pathway level. Unfortunately, methods optimized for bulk transcriptomics perform poorly on scRNA-seq data and progress on single cell-specific techniques has been limited. Importantly, no existing methods support cell-level gene set inference. To address this challenge, we developed a new gene set testing method, Variance-adjusted Mahalanobis (VAM), that integrates with the Seurat framework and can accommodate the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data. The VAM method computes cell-specific pathway scores to transform a cell-by-gene matrix into a cell-by-pathway matrix that can be used for both data visualization and statistical enrichment analysis. Because the distribution of these scores under the null of uncorrelated technical noise has an accurate gamma approximation, both population and cell-level inference is supported. As demonstrated using simulated and real scRNA-seq data, the VAM method provides superior classification accuracy at a lower computation cost relative to existing single sample gene set testing approaches.

Genetic Variants Contributing to Early Recurrent Pregnancy Loss Etiology Identified by Sequencing Approaches

Recurrent pregnancy loss (RPL) affects up to 5% of couples. It is believed that genetic factors contribute to the disease's etiology and pathophysiology. Hundreds of genes represent coherent RPL candidates due to mammalian implantation's inherent complexity. Sanger sequencing (direct sequencing) of candidate genes has identified potential RPL causative genes (and variants), including those regulating embryo implantation and pregnancy maintenance. Although this approach is a reliable technique, the simultaneous analysis of large genomic regions is challenging. Next-generation sequencing (NGS) technology has thus emerged as a useful alternative for determining genetic variants and transcriptomic disturbances contributing to monogenic and polygenic diseases pathogenesis. However, interpreting results remains challenging as NGS experiments provide an enormous amount of complex data. The molecular aspects of specific diseases must be fully understood for accurate interpretation of NGS data. This review was thus aimed at describing (for the first time) the most relevant studies involving Sanger and NGS sequencing, leading to the description of variants related to RPL pathogenesis. Successful RPL-related NGS initiatives (including RNAseq-based studies) and future challenges are discussed. We consider that the information given here should be useful for clinicians, scientists, and students to enable a better understanding of RPL etiology. It may also provide a basis for the development of diagnostic/prognostic approaches contributing toward translational medicine.

Genetic Hallmarks and Heterogeneity of Glioblastoma in the Single-Cell Omics Era

Glioblastoma multiforme is the most common and aggressive malignant primary brain tumor. As implied by its name, the disease displays impressive intrinsic heterogeneity. Among other complications, inter- and intratumoral diversity hamper glioblastoma research and therapy, typically leaving patients with little hope for long-term survival. Extensive genetic analyses, including omics, characterize several recurrent mutations. However, confounding factors mask crucial aspects of the pathology to conventional bulk approaches. In recent years, single-cell omics have made their first appearance in cancer research, and the methodology is about to reach its full potential for glioblastoma too. Here, recent glioblastoma single-cell omics investigations are reviewed, and most promising routes toward less grim prognoses and more efficient therapeutics are discussed.

Single-Cell Analysis of Circulating Tumor Cells: How Far Have We Come in the -Omics Era?

Tumor cells detach from the primary tumor or metastatic sites and enter the peripheral blood, often causing metastasis. These cells, named Circulating Tumor Cells (CTCs), display the same spatial and temporal heterogeneity as the primary tumor. Since CTCs are involved in tumor progression, they represent a privileged window to disclose mechanisms of metastases, while -omic analyses at the single-cell level allow dissection of the complex relationships between the tumor subpopulations and the surrounding normal tissue. However, in addition to reporting the proof of concept that we can query CTCs to reveal tumor evolution throughout the continuum of treatment for early detection of resistance to therapy, the scientific literature has also been highlighting the disadvantages of CTCs, which hampers a routine use of this approach in clinical practice. To date, an increasing number of CTC technologies, as well as -omics methods, have been employed, mostly lacking strong comparative analyses. The rarity of CTCs also represents a major challenge, because there is no consensus regarding the minimal criteria necessary and sufficient to define an event as CTC; moreover, we cannot often compare data from of one study with that of another. Finally, the availability of an individual tumor profile undermines the traditional histology-based treatment. Applying molecular data for patient benefit implies a collective effort by biologists, bioengineers, and clinicians, to create tools to interpret molecular data and manage precision medicine in every single patient. Herein, we focus on the most recent findings in CTC −omics to learn how far we have come.

Single-Cell Applications of Next-Generation Sequencing

The single cell is considered the basic unit of biology, and the pursuit of understanding how heterogeneous populations of cells can functionally coexist in tissues, organisms, microbial ecosystems, and even cancer, makes them the subject of intense study. Next-generation sequencing (NGS) of RNA and DNA has opened a new frontier of (single)-cell biology. Hundreds to millions of cells now can be assayed in parallel, providing the molecular profile of each cell in its milieu inexpensively and in a manner that can be analyzed mathematically. The goal of this article is to provide a high-level overview of single-cell sequencing for the nonexpert and show how its applications are influencing both basic and applied clinical studies in embryology, developmental genetics, and cancer.

A mathematical model of combined CD8 T cell costimulation by 4-1BB (CD137) and OX40 (CD134) receptors

Combined agonist stimulation of the TNFR costimulatory receptors 4-1BB (CD137) and OX40(CD134) has been shown to generate supereffector CD8 T cells that clonally expand to greater levels, survive longer, and produce a greater quantity of cytokines compared to T cells stimulated with an agonist of either costimulatory receptor individually. In order to understand the mechanisms for this effect, we have created a mathematical model for the activation of the CD8 T cell intracellular signaling network by mono- or dual-costimulation. We show that supereffector status is generated via downstream interacting pathways that are activated upon engagement of both receptors, and in silico simulations of the model are supported by published experimental results. The model can thus be used to identify critical molecular targets of T cell dual-costimulation in the context of cancer immunotherapy.

Accurate estimation of cell-type composition from gene expression data

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations.

Bulk RNA-seq data harbors valuable information about gene expression levels from different cell types in tissue samples. Here, the authors develop DWLS, a computational method for estimating cell-type composition of bulk data by leveraging single-cell RNA-seq-derived cell-type signatures.

Next-Generation Sequencing Strategies

More than a decade ago, the term "next-generation" sequencing was coined to describe what was, at the time, revolutionary new methods to sequence RNA and DNA at a faster pace and cheaper cost than could be performed by standard bench-top protocols. Since then, the field of DNA sequencing has evolved at a rapid pace, with new breakthroughs allowing capacity to exponentially increase and cost to dramatically decrease. As genome-scale sequencing has become routine, a paradigm shift is occurring in genomics, which uses the power of high-throughput, rapid sequencing power with large-scale studies. These new approaches to genetic discovery will provide direct impact to fields such as personalized medicine, evolution, and biodiversity. This work reviews recent technology advances and methods in next-generation sequencing and highlights current large-scale sequencing efforts driving the evolution of the genomics space.

Machine learning and statistical methods for clustering single-cell RNA-sequencing data

 

Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics.

Availability

All the source code and data are available at https://github.com/kuanglab/single-cell-review.

Application of single-cell sequencing in human cancer

Precision medicine is emerging as a cornerstone of future cancer care with the objective of providing targeted therapies based on the molecular phenotype of each individual patient. Traditional bulk-level molecular phenotyping of tumours leads to significant information loss, as the molecular profile represents an average phenotype over large numbers of cells, while cancer is a disease with inherent intra-tumour heterogeneity at the cellular level caused by several factors, including clonal evolution, tissue hierarchies, rare cells and dynamic cell states. Single-cell sequencing provides means to characterize heterogeneity in a large population of cells and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and probability of treatment response. Single-cell sequencing methods are now reliable enough to be used in many research laboratories, and we are starting to see applications of these technologies for characterization of human primary cancer cells. In this review, we provide an overview of studies that have applied single-cell sequencing to characterize human cancers at the single-cell level, and we discuss some of the current challenges in the field.

Genetic Variants Contributing to Early Recurrent Pregnancy Loss Etiology Identified by Sequencing Approaches

Recurrent pregnancy loss (RPL) affects up to 5% of couples. It is believed that genetic factors contribute to the disease's etiology and pathophysiology. Hundreds of genes represent coherent RPL candidates due to mammalian implantation's inherent complexity. Sanger sequencing (direct sequencing) of candidate genes has identified potential RPL causative genes (and variants), including those regulating embryo implantation and pregnancy maintenance. Although this approach is a reliable technique, the simultaneous analysis of large genomic regions is challenging. Next-generation sequencing (NGS) technology has thus emerged as a useful alternative for determining genetic variants and transcriptomic disturbances contributing to monogenic and polygenic diseases pathogenesis. However, interpreting results remains challenging as NGS experiments provide an enormous amount of complex data. The molecular aspects of specific diseases must be fully understood for accurate interpretation of NGS data. This review was thus aimed at describing (for the first time) the most relevant studies involving Sanger and NGS sequencing, leading to the description of variants related to RPL pathogenesis. Successful RPL-related NGS initiatives (including RNAseq-based studies) and future challenges are discussed. We consider that the information given here should be useful for clinicians, scientists, and students to enable a better understanding of RPL etiology. It may also provide a basis for the development of diagnostic/prognostic approaches contributing toward translational medicine.

Single Cell Omics of Breast Cancer: An Update on Characterization and Diagnosis

Breast cancer is recognized for its different clinical behaviors and patient outcomes, regardless of common histopathological features at diagnosis. The heterogeneity and dynamics of breast cancer undergoing clonal evolution produces cells with distinct degrees of drug resistance and metastatic potential. Presently, single cell analysis have made outstanding advancements, overshadowing the hurdles of heterogeneity linked with vast populations. The speedy progression in sequencing analysis now allow unbiased, high-output and high-resolution elucidation of the heterogeneity from individual cell within a population. Classical therapeutics strategies for individual patients are governed by the presence and absence of expression pattern of the estrogen and progesterone receptors and human epidermal growth factor receptor 2. However, such tactics for clinical classification have fruitfulness in selection of targeted therapies, short-term patient responses but unable to predict the long-term survival. In any phenotypic alterations, like breast cancer disease, molecular signature have proven its implication, as we aware that individual cell's state is regulated at diverse levels, such as DNA, RNA and protein, by multifaceted interplay of intrinsic biomolecules pathways existing in the organism and extrinsic stimuli such as ambient environment. Thus for complete understanding, complete profiling of single cell requires a synchronous investigations from different levels (multi-omics) to avoid incomplete information produced from single cell. In this article, initially we briefed on novel updates of various methods available to explore omics and then we finally pinpointed on various omics (i.e. genomics, transcriptomics, epigenomics, proteomics and metabolomics) data and few special aspects of circulating tumor cells, disseminated tumor cells and cancer stem cells, so far available from various studies that can be used for better management of breast cancer patients.

Understanding tumor ecosystems by single-cell sequencing: promises and limitations

Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.

Luminescent nanomaterials for droplet tracking in a microfluidic trapping array

The use of high-throughput multiplexed screening platforms has attracted significant interest in the field of on-site disease detection and diagnostics for their capability to simultaneously interrogate single-cell responses across different populations. However, many of the current approaches are limited by the spectral overlap between tracking materials (e.g., organic dyes) and commonly used fluorophores/biochemical stains, thus restraining their applications in multiplexed studies. This work demonstrates that the downconversion emission spectra offered by rare earth (RE)-doped β-hexagonal NaYF₄ nanoparticles (NPs) can be exploited to address this spectral overlap issue. Compared to organic dyes and other tracking materials where the excitation and emission is separated by tens of nanometers, RE elements have a large gap between excitation and emission which results in their spectral independence from the organic dyes. As a proof of concept, two differently doped NaYF₄ NPs (europium: Eu³⁺, and terbium: Tb³⁺) were employed on a fluorescent microscopy-based droplet microfluidic trapping array to test their feasibility as spectrally independent droplet trackers. The luminescence tracking properties of Eu³⁺-doped (red emission) and Tb³⁺-doped (green emission) NPs were successfully characterized by co-encapsulating with genetically modified cancer cell lines expressing green or red fluorescent proteins (GFP and RFP) in addition to a mixed population of live and dead cells stained with ethidium homodimer. Detailed quantification of the luminescent and fluorescent signals was performed to confirm no overlap between each of the NPs and between NPs and cells. Thus, the spectral independence of Eu³⁺-doped and Tb³⁺-doped NPs with each other and with common fluorophores highlights the potential application of this novel technique in multiplexed systems, where many such luminescent NPs (other doped and co-doped NPs) can be used to simultaneously track different input conditions on the same platform. Graphical abstract ᅟ.

scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

Motivation

Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data.

Results

Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using 'single-cell energy' and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are-in combination-more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates.

Availability and implementation

A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath.

Supplementary information

Supplementary data are available at Bioinformatics online.

[Possibilities and limitations of molecular pathology in dermatohistology]

Malignant tumours, infections caused by microorganisms or genodermatoses are diagnosed with additional help of molecular pathology methods. Polymerase chain reaction (PCR), sequencing and in situ hybridisations play an important role. It remains to be seen if methods such as "liquid biopsies" or "single cell genomics" can be developed as routine diagnostics. High technical efforts, high costs and no possibility for resistency testing is accompanied by fast verification, high sensitivity and high specificity. Overall, molecular pathology results have to be combined with the clinical picture, histology or immunohistochemistry and culturing results to achieve a correct diagnosis for the patient.

Targeted Tumor Therapy Remixed-An Update on the Use of Small-Molecule Drugs in Combination Therapies

Over the last decade, the treatment of tumor patients has been revolutionized by the highly successful introduction of novel targeted therapies, in particular small-molecule kinase inhibitors and monoclonal antibodies, as well as by immunotherapies. Depending on the mutational status, BRAF and MEK inhibitor combinations or immune checkpoint inhibitors are current first-line treatments for metastatic melanoma. However, despite great improvements of survival rates limitations due to tumor heterogeneity, primary and acquired therapy resistance, immune evasion, and economical considerations will need to be overcome. Accordingly, ongoing clinical trials explore the individualized use of small-molecule drugs in new targeted therapy combinations based on patient parameters and tumor biopsies. With focus on melanoma therapy this review aims at providing a comprehensive overview of such novel alternative and combinational therapy strategies currently emerging from basic research. The molecular principles and drug classes that may hold promise for improved tumor therapy combination regimens including kinase inhibition, induction of apoptosis, DNA-damage response inhibition, epigenetic reprogramming, telomerase inhibition, redox modulation, metabolic reprogramming, proteasome inhibition, cancer stem cell transdifferentiation, immune cell signaling modulation, and others, are explained in brief. In addition, relevant targeted therapy combinations in current clinical trials and individualized treatment strategies are highlighted.

GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection

Single-cell analysis is a powerful tool for dissecting the cellular composition within a tissue or organ. However, it remains difficult to detect rare and common cell types at the same time. Here, we present a new computational method, GiniClust2, to overcome this challenge. GiniClust2 combines the strengths of two complementary approaches, using the Gini index and Fano factor, respectively, through a cluster-aware, weighted ensemble clustering technique. GiniClust2 successfully identifies both common and rare cell types in diverse datasets, outperforming existing methods. GiniClust2 is scalable to large datasets.

Sensitivity to sequencing depth in single-cell cancer genomics

BACKGROUND

Querying cancer genomes at single-cell resolution is expected to provide a powerful framework to understand in detail the dynamics of cancer evolution. However, given the high costs currently associated with single-cell sequencing, together with the inevitable technical noise arising from single-cell genome amplification, cost-effective strategies that maximize the quality of single-cell data are critically needed. Taking advantage of previously published single-cell whole-genome and whole-exome cancer datasets, we studied the impact of sequencing depth and sampling effort towards single-cell variant detection.

METHODS

Five single-cell whole-genome and whole-exome cancer datasets were independently downscaled to 25, 10, 5, and 1× sequencing depth. For each depth level, ten technical replicates were generated, resulting in a total of 6280 single-cell BAM files. The sensitivity of variant detection, including structural and driver mutations, genotyping, clonal inference, and phylogenetic reconstruction to sequencing depth was evaluated using recent tools specifically designed for single-cell data.

RESULTS

Altogether, our results suggest that for relatively large sample sizes (25 or more cells) sequencing single tumor cells at depths > 5× does not drastically improve somatic variant discovery, characterization of clonal genotypes, or estimation of single-cell phylogenies.

CONCLUSIONS

We suggest that sequencing multiple individual tumor cells at a modest depth represents an effective alternative to explore the mutational landscape and clonal evolutionary patterns of cancer genomes.

Pseudotime Dynamics in Melanoma Single-Cell Transcriptomes Reveals Different Mechanisms of Tumor Progression

Single-cell transcriptomics has been used for analysis of heterogeneous populations of cells during developmental processes and for analysis of tumor cell heterogeneity. More recently, analysis of pseudotime (PT) dynamics of heterogeneous cell populations has been established as a powerful concept to study developmental processes. Here we perform PT analysis of 3 melanoma short-term cultures with different genetic backgrounds to study specific and concordant properties of PT dynamics of selected cellular programs with impact on melanoma progression. Overall, in our setting of melanoma cells PT dynamics towards higher tumor malignancy appears to be largely driven by cell cycle genes. Single cells of all three short-term cultures show a bipolar expression of microphthalmia-associated transcription factor (MITF) and AXL receptor tyrosine kinase (AXL) signatures. Furthermore, opposing gene expression changes are observed for genes regulated by epigenetic mechanisms suggesting epigenetic reprogramming during melanoma progression. The three melanoma short-term cultures show common themes of PT dynamics such as a stromal signature at initiation, bipolar expression of the MITF/AXL signature and opposing regulation of poised and activated promoters. Differences are observed at the late stage of PT dynamics with high, low or intermediate MITF and anticorrelated AXL signatures. These findings may help to identify targets for interference at different stages of tumor progression.

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.

Nuclear Mechanopathology and Cancer Diagnosis

Abnormalities in nuclear and chromatin organization are hallmarks of many diseases including cancer. In this review, we highlight our understanding of how the cellular microenvironment regulates nuclear morphology and, with it, the spatial organization of chromosomes and genes, resulting in cell type-specific genomic programs. We also discuss the molecular basis for maintaining nuclear and genomic integrity and how alterations in nuclear mechanotransduction pathways result in various diseases. Finally, we highlight the importance of digital pathology based on nuclear morphometric features combined with single-cell genomics for early cancer diagnostics.

Application of Single Cell Sequencing in Cancer

Cancer is a heterogenetic disease at both the level of clinical manifestation and the level of the genome. Single-cell sequencing provides an unprecedented means of characterizing the intra-tumor heterogeneity and detecting and analyzing the genomes of cancer cells. These data will help to reconstruct the understanding of the evolutionary lineage of cancer cells. In the future, single-cell technology is believed to be a useful tool in diagnostic and prognostic application in oncology. The application of single cell technology in clinics will make it possible to detect cancer non-invasively at early stages and to develop precision medicine. In this chapter, we review the research and application status of the single cell technology in cancer.

Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis

Current cancer diagnosis employs various nuclear morphometric measures. While these have allowed accurate late-stage prognosis, early diagnosis is still a major challenge. Recent evidence highlights the importance of alterations in mechanical properties of single cells and their nuclei as critical drivers for the onset of cancer. We here present a method to detect subtle changes in nuclear morphometrics at single-cell resolution by combining fluorescence imaging and deep learning. This assay includes a convolutional neural net pipeline and allows us to discriminate between normal and human breast cancer cell lines (fibrocystic and metastatic states) as well as normal and cancer cells in tissue slices with high accuracy. Further, we establish the sensitivity of our pipeline by detecting subtle alterations in normal cells when subjected to small mechano-chemical perturbations that mimic tumor microenvironments. In addition, our assay provides interpretable features that could aid pathological inspections. This pipeline opens new avenues for early disease diagnostics and drug discovery.

Precision Oncology: The Road Ahead

Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and nongenetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single cell analysis of tumor specimens. These may ultimately lead to the development of next-generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.

Challenges and emerging directions in single-cell analysis

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances and look to future opportunities.