Volumetric compression develops noise-driven single-cell heterogeneity

Spatially defined microenvironment for engineering organoids

In the intricately defined spatial microenvironment, a single fertilized egg remarkably develops into a conserved and well-organized multicellular organism. This observation leads us to hypothesize that stem cells or other seed cell types have the potential to construct fully structured and functional tissues or organs, provided the spatial cues are appropriately configured. Current organoid technology, however, largely depends on spontaneous growth and self-organization, lacking systematic guided intervention. As a result, the structures replicated in vitro often emerge in a disordered and sparse manner during growth phases. Although existing organoids have made significant contributions in many aspects, such as advancing our understanding of development and pathogenesis, aiding personalized drug selection, as well as expediting drug development, their potential in creating large-scale implantable tissue or organ constructs, and constructing multicomponent microphysiological systems, together with functioning at metabolic levels remains underutilized. Recent discoveries have demonstrated that the spatial definition of growth factors not only induces directional growth and migration of organoids but also leads to the formation of assembloids with multiple regional identities. This opens new avenues for the innovative engineering of higher-order organoids. Concurrently, the spatial organization of other microenvironmental cues, such as physical stresses, mechanical loads, and material composition, has been minimally explored. This review delves into the burgeoning field of organoid engineering with a focus on potential spatial microenvironmental control. It offers insight into the molecular principles, expected outcomes, and potential applications, envisioning a future perspective in this domain.

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

Design principles of regulatory networks underlying epithelial mesenchymal plasticity in cancer cells

Phenotypic plasticity is a hallmark of cancer and drives metastatic disease and drug resistance. The dynamics of epithelial mesenchymal plasticity is driven by complex interactions involving multiple feedback loops in underlying networks operating at multiple regulatory levels such as transcriptional and epigenetic. The past decade has witnessed a surge in systems level analysis of structural and dynamical traits of these networks. Here, we highlight the key insights elucidated from such efforts-a) multistability in gene regulatory networks and the co-existence of many hybrid phenotypes, thus enabling a landscape with multiple 'attractors', b) mutually antagonistic 'teams' of genes in these networks, shaping the rates of cell state transition in this landscape, and c) chromatin level changes that can alter the landscape, thus controlling reversibility of cell state transitions, allowing cellular memory in the context of epithelial mesenchymal plasticity in cancer cells. Such approaches, in close integration with high-throughput longitudinal data, have improved our understanding of the dynamics of cell state transitions implicated in tumor cell plasticity.

Integration of single-cell sequencing and bulk transcriptome data develops prognostic markers based on PCLAF+ stem-like tumor cells using artificial neural network in gastric cancer

Background

Gastric cancer stem cells (GCSCs) are important tumour cells involved in tumourigenesis and gastric cancer development. However, their clinical value remains unclear due to the limitations of the available technologies. This study aims to explore the clinical significance of GCSCs, their connection to the tumour microenvironment, and their underlying molecular mechanisms.

Methods

Stem-like tumour cells were identified by mining single-cell transcriptomic data from multiple samples. Integrated analysis of single-cell and bulk transcriptome data was performed to analyse the role of stem-like tumour cells in predicting clinical outcomes by introducing the intermediate variable mRNA stemness degree (SD). Consensus clustering analysis was performed to develop an SD-related molecular classification strategy to assess the clinical characteristics in gastric cancer. A prognostic model was constructed using a customized approach that comprehensively considered SD-related gene signatures based on an artificial neural network.

Results

By analysing single-cell data and validating immunofluorescence results, we identified a PCLAF+ stem-like tumour cell population in GC. By calculating SD, we observed that PCLAF+ stem-like tumour cells were associated with poor prognosis and certain clinical features. The SD was negatively correlated with the abundance of most immune cell types. Furthermore, we proposed an SD-related classification method and prognostic model. In addition, the customised prognostic model can be used to predict whether a patient respond to PD-1/PD-L1 immunotherapy.

Conclusion

We identified a cluster of stem-like cells and elucidated their clinical significance, highlighting the possibility of their use as immunotherapeutic targets.

Compressive stresses in cancer: characterization and implications for tumour progression and treatment

Beyond their many well-established biological aberrations, solid tumours create an abnormal physical microenvironment that fuels cancer progression and confers treatment resistance. Mechanical forces impact tumours across a range of biological sizes and timescales, from rapid events at the molecular level involved in their sensing and transmission, to slower and larger-scale events, including clonal selection, epigenetic changes, cell invasion, metastasis and immune response. Owing to challenges with studying these dynamic stimuli in biological systems, the mechanistic understanding of the effects and pathways triggered by abnormally elevated mechanical forces remains elusive, despite clear correlations with cancer pathophysiology, aggressiveness and therapeutic resistance. In this Review, we examine the emerging and diverse roles of physical forces in solid tumours and provide a comprehensive framework for understanding solid stress mechanobiology. We first review the physiological importance of mechanical forces, especially compressive stresses, and discuss their defining characteristics, biological context and relative magnitudes. We then explain how abnormal compressive stresses emerge in tumours and describe the experimental challenges in investigating these mechanically induced processes. Finally, we discuss the clinical translation of mechanotherapeutics that alleviate solid stresses and their potential to synergize with chemotherapy, radiotherapy and immunotherapies.

CancerSCEM 2.0: an updated data resource of single-cell expression map across various human cancers

The field of single-cell RNA sequencing (scRNA-seq) has advanced rapidly in the past decade, generating significant amounts of valuable data for researchers to study complex tumor profiles. This data is crucial for gaining innovative insights into cancer biology. CancerSCEM (https://ngdc.cncb.ac.cn/cancerscem) is a public resource that integrates, analyzes and visualizes scRNA-seq data related to cancer, and it provides invaluable support to numerous cancer-related studies. With CancerSCEM 2.0, scRNA-seq data have increased from 208 to 1466 datasets, covering tumor, matching-normal and peripheral blood samples across 127 research projects and 74 cancer types. The new version of this resource enhances transcriptome analysis by adding copy number variation evaluation, transcription factor enrichment, pseudotime trajectory construction, and diverse biological feature scoring. It also introduces a new cancer metabolic map at the single-cell level, providing an intuitive understanding of metabolic regulation across different cancer types. CancerSCEM 2.0 has a more interactive analysis platform, including four modules and 14 analytical functions, allowing researchers to perform cancer scRNA-seq data analyses in various dimensions. These enhancements are expected to expand the usability of CancerSCEM 2.0 to a broader range of cancer research and clinical applications, potentially revolutionizing our understanding of cancer mechanisms and treatments.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

Protocol to encapsulate cerebral organoids with alginate hydrogel shell to induce volumetric compression

In vitro organoids, including cerebral organoids, are usually developed without mechanical compression, which may contribute to a delay in maturation. Here, we present a protocol for encapsulating cerebral organoids with a thin shell of low-concentration alginate hydrogel. We describe steps for organoid generation, microfluidic chip culture, Matrigel coating, expansion culture, and alginate encapsulation. We then detail procedures for maturation culture and organoid characterization. The moderate compressive stimulation that the shell provides promotes cell proliferation and neuronal maturation. For complete details on the use and execution of this protocol, please refer to Tang et al.1.

Cancer spreading patterns based on epithelial-mesenchymal plasticity

Introduction: Metastasis is a major cause of cancer-related deaths, underscoring the necessity to discern the rules and patterns of cancer cell spreading. Epithelial-mesenchymal plasticity contributes to cancer aggressiveness and metastasis. Despite establishing key determinants of cancer aggressiveness and metastatic ability, a comprehensive understanding of the underlying mechanism is unknown. We aimed to propose a classification system for cancer cells based on epithelial-mesenchymal plasticity, focusing on hysteresis of the epithelial-mesenchymal transition and the hybrid epithelial/mesenchymal phenotype. Methods: We extensively reviewed the concept of epithelial-mesenchymal plasticity, specifically considering the hysteresis of the epithelial-mesenchymal transition and the hybrid epithelial/mesenchymal phenotype. Results: In this review and hypothesis article, based on epithelial-mesenchymal plasticity, especially the hysteresis of epithelial-mesenchymal transition and the hybrid epithelial/mesenchymal phenotype, we proposed a classification of cancer cells, indicating that cancer cells with epithelial-mesenchymal plasticity potential could be classified into four types: irreversible hysteresis, weak hysteresis, strong hysteresis, and hybrid epithelial/mesenchymal phenotype. These four types of cancer cells had varied biology, spreading features, and prognoses. Discussion: Our results highlight that the proposed classification system offers insights into the diverse behaviors of cancer cells, providing implications for cancer aggressiveness and metastasis.

Increased heterogeneity in expression of genes associated with cancer progression and drug resistance

Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.

Volumetric compression by heterogeneous scaffold embedding promotes cerebral organoid maturation and does not impede growth

Although biochemical regulation has been extensively studied in organoid modeling protocols, the role of mechanoregulation in directing stem cell fate and organoid development has been relatively unexplored. To accurately replicate the dynamic organoid development observed in nature, in this study, we present a method of heterogeneous embedding using an alginate-shell-Matrigel-core system. This approach allows for cell-Matrigel remodeling by the inner layer and provides short-term moderate-normal compression through the soft alginate outer layer. Our results show that the time-limited confinement contributes to increased expression of neuronal markers such as neurofilament (NF) and microtubule-associated protein 2 (MAP2). Compared with non-alginate embedding and alginate compression groups, volume growth remains unimpeded. Our findings demonstrate the temporary mechanical regulation of cerebral organoid growth, which exhibits a regular growth profile with enhanced maturation. These results highlight the importance and potential practical applications of mechanoregulation in the establishment of brain organoids. A record of this paper's transparent peer review process is included in the supplemental information.

Adaptation to volumetric compression drives hepatoblastoma cells to an apoptosis-resistant and invasive phenotype

Liver cancer involves tumor cells rapidly growing within a packed tissue environment. Patient tumor tissues reveal densely packed and deformed cells, especially at tumor boundaries, indicative of physical crowding and compression. It is not well understood how these physical signals modulate tumor evolution and therapeutic susceptibility. Here we investigate the impact of volumetric compression on liver cancer (HepG2) behavior. We find that conditioning cells under a highly compressed state leads to major transcriptional reprogramming, notably the loss of hepatic markers, the epithelial-to-mesenchymal transition (EMT)-like changes, and altered calcium signaling-related gene expression, over the course of several days. Biophysically, compressed cells exhibit increased Rac1-mediated cell spreading and cell-extracellular matrix interactions, cytoskeletal reorganization, increased YAP and β-catenin nuclear translocation, and dysfunction in cytoplasmic and mitochondrial calcium signaling. Furthermore, compressed cells are resistant to chemotherapeutics and desensitized to apoptosis signaling. Apoptosis sensitivity can be rescued by stimulated calcium signaling. Our study demonstrates that volumetric compression is a key microenvironmental factor that drives tumor evolution in multiple pathological directions and highlights potential countermeasures to re-sensitize therapy-resistant cells.

Significance statement

Compression can arise as cancer cells grow and navigate within the dense solid tumor microenvironment. It is unclear how compression mediates critical programs that drive tumor progression and therapeutic complications. Here, we take an integrative approach in investigating the impact of compression on liver cancer. We identify and characterize compressed subdomains within patient tumor tissues. Furthermore, using in vitro systems, we induce volumetric compression (primarily via osmotic pressure but also via mechanical force) on liver cancer cells and demonstrate significant molecular and biophysical changes in cell states, including in function, cytoskeletal signaling, proliferation, invasion, and chemoresistance. Importantly, our results show that compressed cells have impaired calcium signaling and acquire resistance to apoptosis, which can be countered via calcium mobilization.

Compression drives diverse transcriptomic and phenotypic adaptations in melanoma

Physical forces are prominent during tumor progression. However, it is still unclear how they impact and drive the diverse phenotypes found in cancer. Here, we apply an integrative approach to investigate the impact of compression on melanoma cells. We apply bioinformatics to screen for the most significant compression-induced transcriptomic changes and investigate phenotypic responses. We show that compression-induced transcriptomic changes are associated with both improvement and worsening of patient prognoses. Phenotypically, volumetric compression inhibits cell proliferation and cell migration. It also induces organelle stress and intracellular oxidative stress and increases pigmentation in malignant melanoma cells and normal human melanocytes. Finally, cells that have undergone compression become more resistant to cisplatin treatment. Our findings indicate that volumetric compression is a double-edged sword for melanoma progression and drives tumor evolution.

Enhancing single-cell biology through advanced AI-powered microfluidics

Microfluidic technology has largely benefited both fundamental biological research and translational clinical diagnosis with its advantages in high-throughput, single-cell resolution, high integrity, and wide-accessibility. Despite the merits we obtained from microfluidics in the last two decades, the current requirement of intelligence in biomedicine urges the microfluidic technology to process biological big data more efficiently and intelligently. Thus, the current readout technology based on the direct detection of the signals in either optics or electrics was not able to meet the requirement. The implementation of artificial intelligence (AI) in microfluidic technology matches up with the large-scale data usually obtained in the high-throughput assays of microfluidics. At the same time, AI is able to process the multimodal datasets obtained from versatile microfluidic devices, including images, videos, electric signals, and sequences. Moreover, AI provides the microfluidic technology with the capability to understand and decipher the obtained datasets rather than simply obtaining, which eventually facilitates fundamental and translational research in many areas, including cell type discovery, cell signaling, single-cell genetics, and diagnosis. In this Perspective, we will highlight the recent advances in employing AI for single-cell biology and present an outlook on the future direction with more advanced AI algorithms.

The epithelial-mesenchymal plasticity landscape: principles of design and mechanisms of regulation

Epithelial-mesenchymal plasticity (EMP) enables cells to interconvert between several states across the epithelial-mesenchymal landscape, thereby acquiring hybrid epithelial/mesenchymal phenotypic features. This plasticity is crucial for embryonic development and wound healing, but also underlies the acquisition of several malignant traits during cancer progression. Recent research using systems biology and single-cell profiling methods has provided novel insights into the main forces that shape EMP, which include the microenvironment, lineage specification and cell identity, and the genome. Additionally, key roles have emerged for hysteresis (cell memory) and cellular noise, which can drive stochastic transitions between cell states. Here, we review these forces and the distinct but interwoven layers of regulatory control that stabilize EMP states or facilitate epithelial-mesenchymal transitions (EMTs) and discuss the therapeutic potential of manipulating the EMP landscape.

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understanding EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis. Despite increasingly available single-cell RNA sequencing (scRNA-seq) data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from scRNA-seq data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data.

Single-cell transcriptome analysis profiles the expression features of TMEM173 in BM cells of high-risk B-cell acute lymphoblastic leukemia

BACKGROUND

As an essential regulator of type I interferon (IFN) response, TMEM173 participates in immune regulation and cell death induction. In recent studies, activation of TMEM173 has been regarded as a promising strategy for cancer immunotherapy. However, transcriptomic features of TMEM173 in B-cell acute lymphoblastic leukemia (B-ALL) remain elusive.

METHODS

Quantitative real-time PCR (qRT-PCR) and western blotting (WB) were applied to determine the mRNA and protein levels of TMEM173 in peripheral blood mononuclear cells (PBMCs). TMEM173 mutation status was assessed by Sanger sequencing. Single-cell RNA sequencing (scRNA-seq) analysis was performed to explore the expression of TMEM173 in different types of bone marrow (BM) cells.

RESULTS

The mRNA and protein levels of TMEM173 were increased in PBMCs from B-ALL patients. Besides, frameshift mutation was presented in TMEM173 sequences of 2 B-ALL patients. ScRNA-seq analysis identified the specific transcriptome profiles of TMEM173 in the BM of high-risk B-ALL patients. Specifically, expression levels of TMEM173 in granulocytes, progenitor cells, mast cells, and plasmacytoid dendritic cells (pDCs) were higher than that in B cells, T cells, natural killer (NK) cells, and dendritic cells (DCs). Subset analysis further revealed that TMEM173 and pyroptosis effector gasdermin D (GSDMD) restrained in precursor-B (pre-B) cells with proliferative features, which expressed nuclear factor kappa-B (NF-κB), CD19, and Bruton's tyrosine kinase (BTK) during the progression of B-ALL. In addition, TMEM173 was associated with the functional activation of NK cells and DCs in B-ALL.

CONCLUSIONS

Our findings provide insights into the transcriptomic features of TMEM173 in the BM of high-risk B-ALL patients. Targeted activation of TMEM173 in specific cells might provide new therapeutic strategies for B-ALL patients.

Dynamics of Epithelial-Mesenchymal Plasticity: What Have Single-Cell Investigations Elucidated So Far?

Epithelial-mesenchymal plasticity (EMP) is a key driver of cancer metastasis and therapeutic resistance, through which cancer cells can reversibly and dynamically alter their molecular and functional traits along the epithelial-mesenchymal spectrum. While cells in the epithelial phenotype are usually tightly adherent, less metastatic, and drug-sensitive, those in the hybrid epithelial/mesenchymal and/or mesenchymal state are more invasive, migratory, drug-resistant, and immune-evasive. Single-cell studies have emerged as a powerful tool in gaining new insights into the dynamics of EMP across various cancer types. Here, we review many recent studies that employ single-cell analysis techniques to better understand the dynamics of EMP in cancer both in vitro and in vivo. These single-cell studies have underlined the plurality of trajectories cells can traverse during EMP and the consequent heterogeneity of hybrid epithelial/mesenchymal phenotypes seen at both preclinical and clinical levels. They also demonstrate how diverse EMP trajectories may exhibit hysteretic behavior and how the rate of such cell-state transitions depends on the genetic/epigenetic background of recipient cells, as well as the dose and/or duration of EMP-inducing growth factors. Finally, we discuss the relationship between EMP and patient survival across many cancer types. We also present a next set of questions related to EMP that could benefit much from single-cell observations and pave the way to better tackle phenotypic switching and heterogeneity in clinic.

Application of sequential cyclic compression on cancer cells in a flexible microdevice

Mechanical forces shape physiological structure and function within cell and tissue microenvironments, during which cells strive to restore their shape or develop an adaptive mechanism to maintain cell integrity depending on strength and type of the mechanical loading. While some cells are shown to experience permanent plastic deformation after a repetitive mechanical tensile loading and unloading, the impact of such repetitive compression on deformation of cells is yet to be understood. As such, the ability to apply cyclic compression is crucial for any experimental setup aimed at the study of mechanical compression taking place in cell and tissue microenvironments. Here, we demonstrate such cyclic compression using a microfluidic compression platform on live cell actin in SKOV-3 ovarian cancer cells. Live imaging of the actin cytoskeleton dynamics of the compressed cells was performed for varying pressures applied sequentially in ascending order during cell compression. Additionally, recovery of the compressed cells was investigated by capturing actin cytoskeleton and nuclei profiles of the cells at zero time and 24 h-recovery after compression in end point assays. This was performed for a range of mild pressures within the physiological range. Results showed that the phenotypical response of compressed cells during recovery after compression with 20.8 kPa differed observably from that for 15.6 kPa. This demonstrated the ability of the platform to aid in the capture of differences in cell behaviour as a result of being compressed at various pressures in physiologically relevant manner. Differences observed between compressed cells fixed at zero time or after 24 h-recovery suggest that SKOV-3 cells exhibit deformations at the time of the compression, a proposed mechanism cells use to prevent mechanical damage. Thus, biomechanical responses of SKOV-3 ovarian cancer cells to sequential cyclic compression and during recovery after compression could be revealed in a flexible microdevice. As demonstrated in this work, the observation of morphological, cytoskeletal and nuclear differences in compressed and non-compressed cells, with controlled micro-scale mechanical cell compression and recovery and using live-cell imaging, fluorescent tagging and end point assays, can give insights into the mechanics of cancer cells.

Epigenetic memory acquired during long-term EMT induction governs the recovery to the epithelial state

Epithelial-mesenchymal transition (EMT) and its reverse mesenchymal-epithelial transition (MET) are critical during embryonic development, wound healing and cancer metastasis. While phenotypic changes during short-term EMT induction are reversible, long-term EMT induction has been often associated with irreversibility. Here, we show that phenotypic changes seen in MCF10A cells upon long-term EMT induction by TGFβ need not be irreversible, but have relatively longer time scales of reversibility than those seen in short-term induction. Next, using a phenomenological mathematical model to account for the chromatin-mediated epigenetic silencing of the miR-200 family by ZEB family, we highlight how the epigenetic memory gained during long-term EMT induction can slow the recovery to the epithelial state post-TGFβ withdrawal. Our results suggest that epigenetic modifiers can govern the extent and time scale of EMT reversibility and advise caution against labelling phenotypic changes seen in long-term EMT induction as 'irreversible'.

Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages

MOTIVATION

In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior.

RESULTS

We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.7 and YUMMER1.7 mouse melanoma cell lines. Using an unsupervised clustering algorithm, we identified six distinct morphological subclasses. We further observed differences in tumor growth and invasion dynamics across subclasses in an in vitro 3D spheroid model. Compared to existing methods for quantifying 2D or 3D phenotype, our analytical method requires less time, needs no specialized equipment and is capable of much higher throughput, making it ideal for applications such as high-throughput drug screening and clinical diagnosis.

AVAILABILITY AND IMPLEMENTATION

https://github.com/trevor-chan/Melanoma_NetworkMorphology.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell technologies: From research to application

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry.