Enhancer-promoter interactions are reconfigured through the formation of long-range multiway hubs as mouse ES cells exit pluripotency.

Enhancers bind transcription factors, chromatin regulators, and non-coding transcripts to modulate the expression of target genes. Here, we report 3D genome structures of single mouse ES cells as they are induced to exit pluripotency and transition through a formative stage prior to undergoing neuroectodermal differentiation. We find that there is a remarkable reorganization of 3D genome structure where inter-chromosomal intermingling increases dramatically in the formative state. This intermingling is associated with the formation of a large number of multiway hubs that bring together enhancers and promoters with similar chromatin states from typically 5-8 distant chromosomal sites that are often separated by many Mb from each other. In the formative state, genes important for pluripotency exit establish contacts with emerging enhancers within these multiway hubs, suggesting that the structural changes we have observed may play an important role in modulating transcription and establishing new cell identities.

Fluctuations in p53 Signaling Allow Escape from Cell-Cycle Arrest.

Biological signals need to be robust and filter small fluctuations yet maintain sensitivity to signals across a wide range of magnitudes. Here, we studied how fluctuations in DNA damage signaling relate to maintenance of long-term cell-cycle arrest. Using live-cell imaging, we quantified division profiles of individual human cells in the course of 1 week after irradiation. We found a subset of cells that initially establish cell-cycle arrest and then sporadically escape and divide. Using fluorescent reporters and mathematical modeling, we determined that fluctuations in the oscillatory pattern of the tumor suppressor p53 trigger a sharp switch between p21 and CDK2, leading to escape from arrest. Transient perturbation of p53 stability mimicked the noise in individual cells and was sufficient to trigger escape from arrest. Our results show that the self-reinforcing circuitry that mediates cell-cycle transitions can translate small fluctuations in p53 signaling into large phenotypic changes.

Single Live Cell Monitoring of Protein Turnover Reveals Intercellular Variability and Cell-Cycle Dependence of Degradation Rates.

Cells need to reliably control their proteome composition to maintain homeostasis and regulate growth. How protein synthesis and degradation interplay to control protein expression levels remains unclear. Here, we combined a tandem fluorescent timer and pulse-chase protein labeling to disentangle how protein synthesis and degradation control protein homeostasis in single live mouse embryonic stem cells. We discovered substantial cell-cycle dependence in protein synthesis rates and stabilization of a large number of proteins around cytokinesis. Protein degradation rates were highly variable between cells, co-varied within individual cells for different proteins, and were positively correlated with synthesis rates. This suggests variability in proteasome activity as an important source of global extrinsic noise in gene expression. Our approach paves the way toward understanding the complex interplay of synthesis and degradation processes in determining protein levels of individual mammalian cells.

Automated and reproducible cell identification in mass cytometry using neural networks.

The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration.

Aneuploidy in neoplasia: Single-cell data on 83,862 tumors.

Chromosomal aneuploidy, that is, numerical chromosome aberrations, is one of the molecular hallmarks of cancer. However, when neoplasms are studied with sequencing- and array-based approaches, chromosome numbers and ploidy states are typically inferred from bulk DNA data. Furthermore, published molecular estimates of neoplasia-associated aneuploidy often also include genomic imbalances resulting from various types of structural rearrangement, which likely result from other mechanisms than numerical chromosome aberrations. We thus analyzed chromosome numbers using single-cell cytogenetic data from 83,862 tumors, and show that both benign and malignant tumors are highly heterogeneous with regard to deviations from the normal, diploid state. Focusing on the chromosome numbers in 112 specific tumor types, defined by both exact morphologic diagnosis and organ location and from which data from ≥50 cases were available, we found two major clusters: one predominated by near-diploid neoplasms and one by neoplasms with extensive aneuploidy and one or more whole genome doublings. The former cluster included most benign solid tumors, myeloid neoplasms, and malignant gene fusion-associated solid tumors, whereas the latter was predominated by malignant solid tumors and lymphomas. For 16 malignant tumor types, the distribution of chromosome numbers could be compared to TCGA ploidy level data. Cytogenetic and molecular data correlated well, but the former indicates a higher level of clonal heterogeneity. The results presented here suggest shared pathogenetic mechanisms in certain tumor types and provide a reference for molecular analyses.

Single extracellular vesicle research: From cell population to a single cell.

Extracellular vesicles (EVs) are secreted by cells with a membrane structure and complex components such as DNA, RNA and proteins. These biomolecules play an important role in cell communication, cell proliferation, cell migration, vascularization, immune response and other physiological and pathological processes. Most current research on EVs focused on populations of EVs. Heterogeneity of EVs is neglected. Considering the heterogeneity of single EVs may offer critical molecular insights into cell-cell interactions, it is necessary to enhance our understanding about molecular characteristics from EVs derived from cell population to a single EV of derived from a single cell. This transformation is expected to provide a new insight into the understanding of cellular biology and the accurate description of the law of disease progress. In this article, we review the current research progress of single EV analysis technology for single EVs derived from cell population (SECP) and discuss its main applications in biological and clinical medicine research. After that, we propose the development direction, main difficulties and application prospect of single EV analysis technology for single EVs derived from single cells (SESC) according to our own research work, to provide new perspectives for the field of EV research.

Embracing computational immunology.

This Special Feature explores the fascinating field of Computational Immunology and features reviews on recent immunology research that used computational tools and concepts to understand the nexus of cancer immunology, autoimmunity and host-pathogen interactions.

High-Precision Single-Cell microRNA Therapy by a Functional Nanopipette with Sensitive Photoelectrochemical Feedback.

This work proposes the concept of single-cell microRNA (miR) therapy and proof-of-concept by engineering a nanopipette for high-precision miR-21-targeted therapy in a single HeLa cell with sensitive photoelectrochemical (PEC) feedback. Targeting the representative oncogenic miR-21, the as-functionalized nanopipette permits direct intracellular drug administration with precisely controllable dosages, and the corresponding therapeutic effects can be sensitively transduced by a PEC sensing interface that selectively responds to the indicator level of cytosolic caspase-3. The experimental results reveal that injection of ca. 4.4 × 10-20 mol miR-21 inhibitor, i.e., 26488 copies, can cause the obvious therapeutic action in the targeted cell. This work features a solution to obtain the accurate knowledge of how a certain miR-drug with specific dosages treats the cells and thus provides an insight into futuristic high-precision clinical miR therapy using personalized medicine, provided that the prerequisite single-cell experiments are courses of personalized customization.

Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning.

Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.

Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel.

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.

SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle.

RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time­dependent kinetic rates during dynamic processes. Here, we present SLAM­Drop­seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol­fixed cells with droplet­based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene­specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust­cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM­Drop­seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single­cell gene expression dynamics in continuous biological processes.

Single-cell response to Wnt signaling activation reveals uncoupling of Wnt target gene expression.

Wnt signaling drives nuclear translocation of ß-catenin and its subsequent association with the DNA-bound TCF/LEF transcription factors, which dictate target gene specificity by recognizing Wnt responsive elements across the genome. ß-Catenin target genes are therefore thought to be collectively activated upon Wnt pathway stimulation. However, this appears in contrast with the non-overlapping patterns of Wnt target gene expression in several contexts, including early mammalian embryogenesis. Here we followed Wnt target gene expression in human embryonic stem cells after Wnt pathway stimulation at a single-cell resolution. Cells changed gene expression program over time consistent with three key developmental events: i) loss of pluripotency, ii) induction of Wnt target genes, and iii) mesoderm specification. Contrary to our expectation, not all cells displayed equal amplitude of Wnt target gene activation; rather, they distributed in a continuum from strong to weak responders when ranked based on the expression of the target AXIN2. Moreover, high AXIN2 did not always correspond to elevated expression of other Wnt targets, which were activated in different proportions in individual cells. The uncoupling of Wnt target gene expression was also identified in single cell transcriptomics profiling of other Wnt-responding cell types, including HEK293T, murine developing forelimbs, and human colorectal cancer. Our finding underlines the necessity to identify additional mechanisms that explain the heterogeneity of the Wnt/ß-catenin-mediated transcriptional outputs in single cells.

Applying single-cell highly multiplexed secretome proteomics to characterize immunotherapeutic products and predict clinical responses.

Genetically and phenotypically identical immune cell populations can be highly heterogenous in terms of their immune functions and protein secretion profiles. The microfluidic chip-based single-cell highly multiplexed secretome proteomics enables characterization of cellular heterogeneity of immune responses at different cellular and molecular layers. Increasing evidence has demonstrated that polyfunctional T cells that simultaneously produce 2+ proteins per cell at the single-cell level are key effector cells that contribute to the development of potent and durable cellular immunity against pathogens and cancers. The functional proteomic technology offers a wide spectrum of cellular function assessment and can uniquely define highly polyfunctional cell subsets with cytokine signatures from live individual cells. This high-dimensional single-cell analysis provides deep dissection into functional heterogeneity and helps identify predictive biomarkers and potential correlates that are crucial for immunotherapeutic product design optimization and personalized immunotherapy development to achieve better clinical outcomes.

Advances and applications of single-cell omics technologies in plant research.

Single-cell sequencing approaches reveal the intracellular dynamics of individual cells and answer biological questions with high-dimensional catalogs of millions of cells, including genomics, transcriptomics, chromatin accessibility, epigenomics, and proteomics data across species. These emerging yet thriving technologies have been fully embraced by the field of plant biology, with a constantly expanding portfolio of applications. Here, we introduce the current technical advances used for single-cell omics, especially single-cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well-executed benchmarking studies to highlight advances made through the application of single-cell omics techniques. Looking forward, we offer a glimpse of additional hurdles and future opportunities that will introduce broad adoption of single-cell sequencing with revolutionary perspectives in plant biology.

Molecular-defined clonal evolution in patients with classical myeloproliferative neoplasms.

Classical myeloproliferative neoplasms (MPNs) are characterized by distinct clinical phenotypes. The discovery of driver mutations in JAK2, CALR and MPL genes provided new insights into their pathogenesis. Next-generation sequencing (NGS) identified additional somatic mutations, most frequently in epigenetic modulator genes. In this study, a cohort of 95 MPN patients was genetically characterized using targeted NGS. Clonal hierarchies of detected mutations were subsequently analysed using colony forming progenitor assays derived from single cells to study mutation acquisition. Further, the hierarchy of mutations within distinct cell lineages was evaluated. NGS revealed mutations in three epigenetic modulator genes (TET2, DNMT3A, ASXL1) as most common co-mutations to the classical driver mutations. JAK2V617F as well as DNMT3A and TET2 mutations were detected as primary events in disease formation and most cases presented with a linear mutation pattern. Mutations appear mostly in the myeloid lineages but can also appear in lymphoid subpopulations. In one case with a double mutant MPL gene, mutations exclusively appeared in the monocyte lineage. Overall, this study confirms the mutational heterogeneity of classical MPNs and highlights the role of JAK2V617F and epigenetic modifier genes as early events in hematologic disease formation.

Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry.

Cervical cancer is a high-risk disease that threatens women's health globally. In this study, we developed the multi-modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical cancer cells (HeLa). With the light-sheet static cytometry, we obtain brightfield (BF) images, fluorescence (FL) images and two-dimensional (2D) light scattering (LS) patterns of single cervical cells. Three feature extraction methods are used to extract multi-modal features based on different data characteristics. Analysis and classification of morphological and textural features demonstrate the potential of intracellular mitochondria in cervical cancer cell classification. The deep learning method is used to automatically extract deep features of label-free LS patterns, and an accuracy of 76.16% for the classification of the above two kinds of cervical cells is obtained, which is higher than the other two single modes (BF and FL). Our multi-modal static cytometry uses a variety of feature extraction and analysis methods to provide the mitochondria as promising internal biomarkers for cervical cancer diagnosis, and to show the promise of label-free, automatic classification of early cervical cancer with deep learning-based 2D light scattering.

Coordination of MAPK and p53 dynamics in the cellular responses to DNA damage and oxidative stress.

In response to different cellular stresses, the transcription factor p53 undergoes different dynamics. p53 dynamics, in turn, control cell fate. However, distinct stresses can generate the same p53 dynamics but different cell fate outcomes, suggesting integration of dynamic information from other pathways is important for cell fate regulation. To determine how MAPK activities affect p53-mediated responses to DNA breaks and oxidative stress, we simultaneously tracked p53 and either ERK, JNK, or p38 activities in single cells. While p53 dynamics were comparable between the stresses, cell fate outcomes were distinct. Combining MAPK dynamics with p53 dynamics was important for distinguishing between the stresses and for generating temporal ordering of cell fate pathways. Furthermore, cross-talk between MAPKs and p53 controlled the balance between proliferation and cell death. These findings provide insight into how cells integrate signaling pathways with distinct temporal patterns of activity to encode stress specificity and drive different cell fate decisions.

Increasing resolution in stress neurobiology: from single cells to complex group behaviors.

Stress can have severe psychological and physiological consequences. Thus, inappropriate regulation of the stress response is linked to the etiology of mood and anxiety disorders. The generation and implementation of preclinical animal models represent valuable tools to explore and characterize the mechanisms underlying the pathophysiology of stress-related psychiatric disorders and the development of novel pharmacological strategies. In this commentary, we discuss the strengths and limitations of state-of-the-art molecular and computational advances employed in stress neurobiology research, with a focus on the ever-increasing spatiotemporal resolution in cell biology and behavioral science. Finally, we share our perspective on future directions in the fields of preclinical and human stress research.

Striking heterogeneity of somatic L1 retrotransposition in single normal and cancerous gastrointestinal cells.

Somatic LINE-1 (L1) retrotransposition has been detected in early embryos, adult brains, and the gastrointestinal (GI) tract, and many cancers, including epithelial GI tumors. We previously found numerous somatic L1 insertions in paired normal and GI cancerous tissues. Here, using a modified method of single-cell analysis for somatic L1 insertions, we studied adenocarcinomas of colon, pancreas, and stomach, and found a variable number of somatic L1 insertions in tumors of the same type from patient to patient. We detected no somatic L1 insertions in single cells of 5 of 10 tumors studied. In three tumors, aneuploid cells were detected by FACS. In one pancreatic tumor, there were many more L1 insertions in aneuploid than in euploid tumor cells. In one gastric cancer, both aneuploid and euploid cells contained large numbers of likely clonal insertions. However, in a second gastric cancer with aneuploid cells, no somatic L1 insertions were found. We suggest that when the cellular environment is favorable to retrotransposition, aneuploidy predisposes tumor cells to L1 insertions, and retrotransposition may occur at the transition from euploidy to aneuploidy. Seventeen percent of insertions were also present in normal cells, similar to findings in genomic DNA from normal tissues of GI tumor patients. We provide evidence that: 1) The number of L1 insertions in tumors of the same type is highly variable, 2) most somatic L1 insertions in GI cancer tissues are absent from normal tissues, and 3) under certain conditions, somatic L1 retrotransposition exhibits a propensity for occurring in aneuploid cells.

The Bladder Tumor Microenvironment Components That Modulate the Tumor and Impact Therapy.

The tumor microenvironment (TME) is complex and involves many different cell types that seemingly work together in helping cancer cells evade immune monitoring and survive therapy. The advent of single-cell sequencing has greatly increased our knowledge of the cell types present in the tumor microenvironment and their role in the developing cancer. This, coupled with clinical data showing that cancer development and the response to therapy may be influenced by drugs that indirectly influence the tumor environment, highlights the need to better understand how the cells present in the TME work together. This review looks at the different cell types (cancer cells, cancer stem cells, endothelial cells, pericytes, adipose cells, cancer-associated fibroblasts, and neuronal cells) in the bladder tumor microenvironment. Their impact on immune activation and on shaping the microenvironment are discussed as well as the effects of hypertensive drugs and anesthetics on bladder cancer.