Live-seq enables temporal transcriptomic recording of single cells

Unraveling the hidden complexity: Exploring dental tissues through single-cell transcriptional profiling

Understanding the composition and function of cells constituting tissues and organs is vital for unraveling biological processes. Single-cell analysis has allowed us to move beyond traditional methods of categorizing cell types. This innovative technology allows the transcriptional and epigenetic profiling of numerous individual cells, leading to significant insights into the development, homeostasis, and pathology of various organs and tissues in both animal models and human samples. In this review, we delve into the outcomes of major investigations using single-cell transcriptomics to decipher the cellular composition of mammalian teeth and periodontal tissues. The recent single-cell transcriptome-based studies have traced in detail the dental epithelium-ameloblast lineage and dental mesenchyme lineages in the mouse incisors and the tooth germ of both mice and humans; unraveled the microenvironment, the identity of niche cells, and cellular intricacies in the dental pulp; shed light on the molecular mechanisms orchestrating root formation; and characterized cellular dynamics of the periodontal ligament. Additionally, cellular components in dental pulps were compared between healthy and carious teeth at a single-cell level. Each section of this review contributes to a comprehensive understanding of tooth biology, offering valuable insights into developmental processes, niche cell identification, and the molecular secrets of the dental environment.

Bioprobe-RNA-seq-microRaman system for deep tracking of the live single-cell metabolic pathway chemometrics

The integration between RNA-sequencing and micro-spectroscopic techniques has recently profiled the advanced transcriptomic discoveries on the cellular level. In the current study, by combining the sensation approach (including bio-molecules structural evaluation, high throughput next-generation sequencing (HT-NGS), and confocal Raman microscopy) the functionality on the single live cancer cells' ferroptosis and apoptosis signaling pathways is visualized. Our study reveals a hydrophobic tunnel by phycocyanin-isoprene molecule (PC-SIM) electrostatic charge within hepatoma cells (HepG2) that activates the ferritin light chain (FTL) and caspase-8 associate protein (CASP8AP2) ferroptosis responsible genes. Moreover, this research proves that PC-vanillin (VAN) stimulation induces the actin-binding factor profilin-1 (PFN1), subsequently in situ tracking its expression at 1139.75 cm-1 microRaman wavenumber. While PC-thymol (THY) induces the lysophospholipase-2 (LYPLA2) (p-value = 0.009) and acetylneuraminate-9-O-acetyltransferase (CASD1) (p-value = 0.022) at 1143.19 cm-1. Our findings establish a new concept to promote the cross-disciplinary use of instant cellular-based detection technology for intermediary evaluating the signaling cellular transcriptome.

Advance and Application of Single-cell Transcriptomics in Auditory Research

Hearing loss and deafness, as a worldwide disability disease, have been troubling human beings. However, the auditory organ of the inner ear is highly heterogeneous and has a very limited number of cells, which are largely uncharacterized in depth. Recently, with the development and utilization of single-cell RNA sequencing (scRNA-seq), researchers have been able to unveil the complex and sophisticated biological mechanisms of various types of cells in the auditory organ at the single-cell level and address the challenges of cellular heterogeneity that are not resolved through by conventional bulk RNA sequencing (bulk RNA-seq). Herein, we reviewed the application of scRNA-seq technology in auditory research, with the aim of providing a reference for the development of auditory organs, the pathogenesis of hearing loss, and regenerative therapy. Prospects about spatial transcriptomic scRNA-seq, single-cell based genome, and Live-seq technology will also be discussed.

Closed-loop experiments and brain machine interfaces with multiphoton microscopy

In the field of neuroscience, the importance of constructing closed-loop experimental systems has increased in conjunction with technological advances in measuring and controlling neural activity in live animals. We provide an overview of recent technological advances in the field, focusing on closed-loop experimental systems where multiphoton microscopy-the only method capable of recording and controlling targeted population activity of neurons at a single-cell resolution in vivo-works through real-time feedback. Specifically, we present some examples of brain machine interfaces (BMIs) using in vivo two-photon calcium imaging and discuss applications of two-photon optogenetic stimulation and adaptive optics to real-time BMIs. We also consider conditions for realizing future optical BMIs at the synaptic level, and their possible roles in understanding the computational principles of the brain.

Computational single-cell methods for predicting cancer risk

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.

Nanobiopsy investigation of the subcellular mtDNA heteroplasmy in human tissues

Mitochondrial function is critical to continued cellular vitality and is an important contributor to a growing number of human diseases. Mitochondrial dysfunction is typically heterogeneous, mediated through the clonal expansion of mitochondrial DNA (mtDNA) variants in a subset of cells in a given tissue. To date, our understanding of the dynamics of clonal expansion of mtDNA variants has been technically limited to the single cell-level. Here, we report the use of nanobiopsy for subcellular sampling from human tissues, combined with next-generation sequencing to assess subcellular mtDNA mutation load in human tissue from mitochondrial disease patients. The ability to map mitochondrial mutation loads within individual cells of diseased tissue samples will further our understanding of mitochondrial genetic diseases.

An inducible genetic tool to track and manipulate specific microglial states reveals their plasticity and roles in remyelination

Recent single-cell RNA sequencing studies have revealed distinct microglial states in development and disease. These include proliferative-region-associated microglia (PAMs) in developing white matter and disease-associated microglia (DAMs) prevalent in various neurodegenerative conditions. PAMs and DAMs share a similar core gene signature. However, the extent of the dynamism and plasticity of these microglial states, as well as their functional significance, remains elusive, partly due to the lack of specific tools. Here, we generated an inducible Cre driver line, Clec7a-CreERT2, that targets PAMs and DAMs in the brain parenchyma. Utilizing this tool, we profiled labeled cells during development and in several disease models, uncovering convergence and context-dependent differences in PAM and DAM gene expression. Through long-term tracking, we demonstrated microglial state plasticity. Lastly, we specifically depleted DAMs in demyelination, revealing their roles in disease recovery. Together, we provide a versatile genetic tool to characterize microglial states in CNS development and disease.

Nanoscale Methods for Longitudinal Extraction of Intracellular Contents

Longitudinal analysis of intracellular contents including gene and protein expression is crucial for deciphering the fundamentally dynamic nature of cells. This offers invaluable insights into complex tissue composition and behavior, and drives progress in disease diagnosis, biomarker discovery, and drug development. Traditional longitudinal analysis workflows, involving the destruction of cells at various timepoints, limit insights to singular moments and fail to account for cellular heterogeneity. Current non-destructive approaches, like temporal modeling with single-cell ribonucleic acid sequencing (RNA-seq) and live-cell fluorescence imaging, either rely on biological assumptions or possess the risk of cellular perturbation. Recent advances in nanoscale technologies for non-destructive intracellular content extraction offer a promising solution to these challenges. These novel methods work at the nanoscale to non-destructively access cellular membranes and can be broadly classified into three mechanisms: tip-facilitated aspiration, membrane-based, and probe-based methods. This perspective focuses on these emerging nanotechnologies for repeated intracellular content extraction. Their potential in longitudinal analysis is discussed, the critical requirements for effective repeated sampling are addressed, and the suitability of each technique for various applications is explored. Furthermore, unresolved challenges in repeated sampling are highlighted to encourage further research in this growing field.

Biomarker Selection for Adaptive Systems

Biomarkers enable objective monitoring of a given cell or state in a biological system and are widely used in research, biomanufacturing, and clinical practice. However, identifying appropriate biomarkers that are both robustly measurable and capture a state accurately remains challenging. We present a framework for biomarker identification based upon observability guided sensor selection. Our methods, Dynamic Sensor Selection (DSS) and Structure-Guided Sensor Selection (SGSS), utilize temporal models and experimental data, offering a template for applying observability theory to unconventional data obtained from biological systems. Unlike conventional methods that assume well-known, fixed dynamics, DSS adaptively select biomarkers or sensors that maximize observability while accounting for the time-varying nature of biological systems. Additionally, SGSS incorporates structural information and diverse data to identify sensors which are resilient against inaccuracies in our model of the underlying system. We validate our approaches by performing estimation on high dimensional systems derived from temporal gene expression data from partial observations.

Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics

Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup. We present high classification accuracy based on the monitored single-cell adhesion kinetic signals. We developed a high-throughput data processing pipeline to build a benchmark database of ~ 4500 single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used with different cell-type selections to test the performance of deep learning-based classification models, reaching above 70-80% depending on the classification task. Beyond testing these models, we aimed to draw interpretable biological insights from their results; thus, we applied a deep neural network visualization method (grad-CAM) to reveal the basis on which these complex models made their decisions. Our proof-of-concept work demonstrated the success of a deep neural network using merely label-free adhesion kinetic data to classify single mammalian cells into different cell types. We propose our method for label-free single-cell profiling and in vitro cancer research involving adhesion. The employed label-free measurement is noninvasive and does not affect cellular functionality. Therefore, it could also be adapted for applications where the selected cells need further processing, such as immune therapy and regenerative medicine.

Complementing Cell Taxonomies with a Multicellular Analysis of Tissues

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.

Single-cell transcriptomics: background, technologies, applications, and challenges

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

One-Shot Single-Cell Proteome and Metabolome Analysis Strategy for the Same Single Cell

Characterizing the profiles of proteome and metabolome at the single-cell level is of great significance in single-cell multiomic studies. Herein, we proposed a novel strategy called one-shot single-cell proteome and metabolome analysis (scPMA) to acquire the proteome and metabolome information in a single-cell individual in one injection of LC-MS/MS analysis. Based on the scPMA strategy, a total workflow was developed to achieve the single-cell capture, nanoliter-scale sample pretreatment, one-shot LC injection and separation of the enzyme-digested peptides and metabolites, and dual-zone MS/MS detection for proteome and metabolome profiling. Benefiting from the scPMA strategy, we realized dual-omic analysis of single tumor cells, including A549, HeLa, and HepG2 cells with 816, 578, and 293 protein groups and 72, 91, and 148 metabolites quantified on average. A single-cell perspective experiment for investigating the doxorubicin-induced antitumor effects in both the proteome and metabolome aspects was also performed.

Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology

Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.

Direct Salmonella injection into enteroid cells allows the study of host-pathogen interactions in the cytosol with high spatiotemporal resolution

Intestinal epithelial cells (IECs) play pivotal roles in nutrient uptake and in the protection against gut microorganisms. However, certain enteric pathogens, such as Salmonella enterica serovar Typhimurium (S. Tm), can invade IECs by employing flagella and type III secretion systems (T3SSs) with cognate effector proteins and exploit IECs as a replicative niche. Detection of flagella or T3SS proteins by IECs results in rapid host cell responses, i.e., the activation of inflammasomes. Here, we introduce a single-cell manipulation technology based on fluidic force microscopy (FluidFM) that enables direct bacteria delivery into the cytosol of single IECs within a murine enteroid monolayer. This approach allows to specifically study pathogen-host cell interactions in the cytosol uncoupled from preceding events such as docking, initiation of uptake, or vacuole escape. Consistent with current understanding, we show using a live-cell inflammasome reporter that exposure of the IEC cytosol to S. Tm induces NAIP/NLRC4 inflammasomes via its known ligands flagellin and T3SS rod and needle. Injected S. Tm mutants devoid of these invasion-relevant ligands were able to grow in the cytosol of IECs despite the absence of T3SS functions, suggesting that, in the absence of NAIP/NLRC4 inflammasome activation and the ensuing cell death, no effector-mediated host cell manipulation is required to render the epithelial cytosol growth-permissive for S. Tm. Overall, the experimental system to introduce S. Tm into single enteroid cells enables investigations into the molecular basis governing host-pathogen interactions in the cytosol with high spatiotemporal resolution.

Gut Metabolites Acting on the Gut-Brain Axis: Regulating the Functional State of Microglia

The gut-brain axis is a communication channel that mediates a complex interplay of intestinal flora with the neural, endocrine, and immune systems, linking gut and brain functions. Gut metabolites, a group of small molecules produced or consumed by biochemical processes in the gut, are involved in central nervous system regulation via the highly interconnected gut-brain axis affecting microglia indirectly by influencing the structure of the gut-brain axis or directly affecting microglia function and activity. Accordingly, pathological changes in the central nervous system are connected with changes in intestinal metabolite levels as well as altered microglia function and activity, which may contribute to the pathological process of each neuroinflammatory condition. Here, we discuss the mechanisms by which gut metabolites, for instance, the bile acids, short-chain fatty acids, and tryptophan metabolites, regulate the structure of each component of the gut-brain axis, and explore the important roles of gut metabolites in the central nervous system from the perspective of microglia. At the same time, we highlight the roles of gut metabolites affecting microglia in the pathogenesis of neurodegenerative diseases and neurodevelopmental disorders. Understanding the relationship between microglia, gut microbiota, neuroinflammation, and neurodevelopmental disorders will help us identify new strategies for treating neuropsychiatric disorders.

Single-cell nanobiopsy enables multigenerational longitudinal transcriptomics of cancer cells

Single-cell RNA sequencing has revolutionized our understanding of cellular heterogeneity, but routine methods require cell lysis and fail to probe the dynamic trajectories responsible for cellular state transitions, which can only be inferred. Here, we present a nanobiopsy platform that enables the injection of exogenous molecules and multigenerational longitudinal cytoplasmic sampling from a single cell and its progeny. The technique is based on scanning ion conductance microscopy (SICM) and, as a proof of concept, was applied to longitudinally profile the transcriptome of single glioblastoma (GBM) brain tumor cells in vitro over 72 hours. The GBM cells were biopsied before and after exposure to chemotherapy and radiotherapy, and our results suggest that treatment either induces or selects for more transcriptionally stable cells. We envision the nanobiopsy will contribute to transforming standard single-cell transcriptomics from a static analysis into a dynamic assay.

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.

Discrete Femtolitre Pipetting with 3D Printed Axisymmetrical Phaseguides

The capacity to precisely pipette femtoliter volumes of liquid enables many applications, for example, to functionalize a nanoscale surface and manipulate fluids inside a single-cell. A pressure-controlled pipetting method is the most preferred, since it enables the widest range of working liquids. However, precisely controlling femtoliter volumes by pressure is challenging. In this work, a new concept is proposed that makes use of axisymmetrical phaseguides inside a microfluidic channel to pipette liquid in discrete steps of known volume. An analytical model for the design of the femtopipettes is developed and verified experimentally. Femtopipettes are fabricated using a multi-scale 3D printing strategy integrating a digital light processing printed part and a two-photon-polymerization printed part. Three different variants are designed and fabricated with pipetting resolutions of 10 picoliters, 180 femtoliters and 50 femtoliters. As a demonstration, controlled amounts of a water-glycerol mixture were first aspirated and then dispensed into a mineral oil droplet.

Hydrodynamic function and spring constant calibration of FluidFM micropipette cantilevers

Fluidic force microscopy (FluidFM) fuses the force sensitivity of atomic force microscopy with the manipulation capabilities of microfluidics by using microfabricated cantilevers with embedded fluidic channels. This innovation initiated new research and development directions in biology, biophysics, and material science. To acquire reliable and reproducible data, the calibration of the force sensor is crucial. Importantly, the hollow FluidFM cantilevers contain a row of parallel pillars inside a rectangular beam. The precise spring constant calibration of the internally structured cantilever is far from trivial, and existing methods generally assume simplifications that are not applicable to these special types of cantilevers. In addition, the Sader method, which is currently implemented by the FluidFM community, relies on the precise measurement of the quality factor, which renders the calibration of the spring constant sensitive to noise. In this study, the hydrodynamic function of these special types of hollow cantilevers was experimentally determined with different instruments. Based on the hydrodynamic function, a novel spring constant calibration method was adapted, which relied only on the two resonance frequencies of the cantilever, measured in air and in a liquid. Based on these results, our proposed method can be successfully used for the reliable, noise-free calibration of hollow FluidFM cantilevers.

Characterizing induced pluripotent stem cells and derived cardiomyocytes: insights from nano scale mass measurements and mechanical properties

Our study reveals that the nano-mechanical measures of elasticity and cell mass change significantly through induced pluripotent stem cell (iPSC) differentiation to cardiomyocytes, providing a reliable method to evaluate such processes. The findings support the importance of identifying these properties, and highlight the potential of AFM for comprehensive characterization of iPSC at the nanoscale.

Resolving cellular dynamics using single-cell temporal transcriptomics

Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.

Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca2+ imaging and electrophysiology

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.

Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA

Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.

Programming human cell fate: overcoming challenges and unlocking potential through technological breakthroughs

In recent years, there have been notable advancements in the ability to programme human cell identity, enabling us to design and manipulate cell function in a Petri dish. However, current protocols for generating target cell types often lack efficiency and precision, resulting in engineered cells that do not fully replicate the desired identity or functional output. This applies to different methods of cell programming, which face similar challenges that hinder progress and delay the achievement of a more favourable outcome. However, recent technological and analytical breakthroughs have provided us with unprecedented opportunities to advance the way we programme cell fate. The Company of Biologists' 2023 workshop on 'Novel Technologies for Programming Human Cell Fate' brought together experts in human cell fate engineering and experts in single-cell genomics, manipulation and characterisation of cells on a single (sub)cellular level. Here, we summarise the main points that emerged during the workshop's themed discussions. Furthermore, we provide specific examples highlighting the current state of the field as well as its trajectory, offering insights into the potential outcomes resulting from the application of these breakthrough technologies in precisely engineering the identity and function of clinically valuable human cells.

An inducible genetic tool for tracking and manipulating specific microglial states in development and disease

Recent single-cell RNA sequencing studies have revealed distinct microglial states in development and disease. These include proliferative region-associated microglia (PAM) in developing white matter and disease-associated microglia (DAM) prevalent in various neurodegenerative conditions. PAM and DAM share a similar core gene signature and other functional properties. However, the extent of the dynamism and plasticity of these microglial states, as well as their functional significance, remains elusive, partly due to the lack of specific tools. Here, we report the generation of an inducible Cre driver line, Clec7a-CreERT2, designed to target PAM and DAM in the brain parenchyma. Utilizing this tool, we profile labeled cells during development and in several disease models, uncovering convergence and context-dependent differences in PAM/DAM gene expression. Through long-term tracking, we demonstrate surprising levels of plasticity in these microglial states. Lastly, we specifically depleted DAM in cuprizone-induced demyelination, revealing their roles in disease progression and recovery.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

CD8 T-cell diversification: Asymmetric cell division and its functional implications

Establishment of cellular diversity is a basic requirement for the development of multicellular organisms. Cellular diversification can be induced by asymmetric cell division (ACD), during which the emerging two daughter cells unequally inherit lineage specific cargo (including transcription factors, receptors for specific signaling inputs, metabolic platforms, and possibly different epigenetic landscapes), resulting in two daughter cells endowed with different fates. While ACD is strongly involved in lineage choices in mammalian stem cells, its role in fate diversification in lineage committed cell subsets that still exhibit plastic potential, such as T-cells, is currently investigated. In this review, we focus predominantly on the role of ACD in fate diversification of CD8 T-cells. Further, we discuss the impact of differential T-cell receptor stimulation strengths and differentiation history on ACD-mediated fate diversification and highlight a particular importance of ACD in the development of memory CD8 T-cells upon high-affinity stimulation conditions.

Large-scale data-driven and physics-based models offer insights into the relationships among the structures, dynamics, and functions of chromosomes

The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression. When the cell changes its identity in the cell-fate decision-making process, extensive rearrangements of chromosome structures occur accompanied by large-scale adaptations of gene expression, underscoring the importance of chromosome dynamics in shaping genome function. Over the last two decades, rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes. In parallel, these enormous data offer valuable opportunities for developing quantitative computational models. Here, we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes. Different from the underlying modeling strategies, these approaches can be classified into data-driven ('top-down') and physics-based ('bottom-up') categories. We discuss their contributions to offering valuable insights into the relationships among the structures, dynamics, and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.

Enablers and challenges of spatial omics, a melting pot of technologies

Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.

CRISPR/Cas-Assisted Nanoneedle Sensor for Adenosine Triphosphate Detection in Living Cells

The clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein (Cas) (CRISPR/Cas) systems have recently emerged as powerful molecular biosensing tools based on their collateral cleavage activity due to their simplicity, sensitivity, specificity, and broad applicability. However, the direct application of the collateral cleavage activity for in situ intracellular detection is still challenging. Here, we debut a CRISPR/Cas-assisted nanoneedle sensor (nanoCRISPR) for intracellular adenosine triphosphate (ATP), which avoids the challenges associated with intracellular collateral cleavage by introducing a two-step process of intracellular target recognition, followed by extracellular transduction and detection. ATP recognition occurs by first presenting in the cell cytosol an aptamer-locked Cas12a activator conjugated to nanoneedles; the recognition event unlocks the activator immobilized on the nanoneedles. The nanoneedles are then removed from the cells and exposed to the Cas12a/crRNA complex, where the activator triggers the cleavage of an ssDNA fluorophore-quencher pair, generating a detectable fluorescence signal. NanoCRISPR has an ATP detection limit of 246 nM and a dynamic range from 1.56 to 50 μM. Importantly, nanoCRISPR can detect intracellular ATP in 30 min in live cells without impacting cell viability. We anticipate that the nanoCRISPR approach will contribute to broadening the biomedical applications of CRISPR/Cas sensors for the detection of diverse intracellular molecules in living systems.

A single-cell transcriptomic atlas characterizes age-related changes of murine cranial stem cell niches

The craniofacial bones provide structural support for the skull and accommodate the vulnerable brain tissue with a protective cavity. The bone tissue undergoes constant turnover, which relies on skeletal stem cells (SSCs) and/or mesenchymal stem cells (MSCs) and their niches. SSCs/MSCs and their perivascular niche within the bone marrow are well characterized in long bones. As for cranial bones, besides bone marrow, the suture mesenchyme has been identified as a unique niche for SSCs/MSCs of craniofacial bones. However, a comprehensive study of the two different cranial stem cell niches at single-cell resolution is still lacking. In addition, during the progression of aging, age-associated changes in cranial stem cell niches and resident cells remain uncovered. In this study, we investigated age-related changes in cranial stem cell niches via single-cell RNA sequencing (scRNA-seq). The transcriptomic profiles and cellular compositions have been delineated, indicating alterations of the cranial bone marrow microenvironment influenced by inflammaging. Moreover, we identified a senescent mesenchymal cell subcluster and several age-related immune cell subclusters by reclustering and pseudotime trajectory analysis, which might be closely linked to inflammaging. Finally, differentially expressed genes (DEGs) and cell-cell communications were analyzed during aging, revealing potential regulatory factors. Overall, this work highlights the age-related changes in cranial stem cell niches, which deepens the current understanding of cranial bone and suture biology and may provide therapeutic targets for antiaging and regenerative medicine.

Bridging live-cell imaging and next-generation cancer treatment

By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.

CRISPR screening in hematology research: from bulk to single-cell level

The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

Phenotypic Switching of Vascular Smooth Muscle Cells in Atherosclerosis

The medial layer of the arterial wall is composed mainly of vascular smooth muscle cells (VSMCs). Under physiological conditions, VSMCs assume a contractile phenotype, and their primary function is to regulate vascular tone. In contrast with terminally differentiated cells, VSMCs possess phenotypic plasticity, capable of transitioning into other cellular phenotypes in response to changes in the vascular environment. Recent research has shown that VSMC phenotypic switching participates in the pathogenesis of atherosclerosis, where the various types of dedifferentiated VSMCs accumulate in the atherosclerotic lesion and participate in the associated vascular remodeling by secreting extracellular matrix proteins and proteases. This review article discusses the 9 VSMC phenotypes that have been reported in atherosclerotic lesions and classifies them into differentiated VSMCs, intermediately dedifferentiated VSMCs, and dedifferentiated VSMCs. It also provides an overview of several methodologies that have been developed for studying VSMC phenotypic switching and discusses their respective advantages and limitations.

Molecular Time Capsules Enable Transcriptomic Recording in Living Cells

Live-cell transcriptomic recording can help reveal hidden cellular states that precede phenotypic transformation. Here we demonstrate the use of protein-based encapsulation for preserving samples of cytoplasmic RNAs inside living cells. These molecular time capsules (MTCs) can be induced to create time-stamped transcriptome snapshots, preserve RNAs after cellular transitions, and enable retrospective investigations of gene expression programs that drive distinct developmental trajectories. MTCs also open the possibility to uncover transcriptomes in difficult-to-reach conditions.

Recent advancements in single-cell metabolic analysis for pharmacological research

Cellular heterogeneity is crucial for understanding tissue biology and disease pathophysiology. Pharmacological research is being advanced by single-cell metabolic analysis, which offers a technique to identify variations in RNA, proteins, metabolites, and drug molecules in cells. In this review, the recent advancement of single-cell metabolic analysis techniques and their applications in drug metabolism and drug response are summarized. High-precision and controlled single-cell isolation and manipulation are provided by microfluidics-based methods, such as droplet microfluidics, microchamber, open microfluidic probe, and digital microfluidics. They are used in tandem with variety of detection techniques, including optical imaging, Raman spectroscopy, electrochemical detection, RNA sequencing, and mass spectrometry, to evaluate single-cell metabolic changes in response to drug administration. The advantages and disadvantages of different techniques are discussed along with the challenges and future directions for single-cell analysis. These techniques are employed in pharmaceutical analysis for studying drug response and resistance pathway, therapeutic targets discovery, and in vitro disease model evaluation.

Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells

Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics-that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFNγ) stimulation. We found that IRF1 responds to low concentration or short duration IFNγ stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFNγ stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression.

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid-Based 3D In Vitro Models

Modeling the heterogeneity of the tumor microenvironment (TME) in vitro is essential to investigating fundamental cancer biology and developing novel treatment strategies that holistically address the factors affecting tumor progression and therapeutic response. Thus, the development of new tools for both in vitro modeling, such as patient-derived organoids (PDOs) and complex 3D in vitro models, and single cell omics analysis, such as single-cell RNA-sequencing, represents a new frontier for investigating tumor heterogeneity. Specifically, the integration of PDO-based 3D in vitro models and single cell analysis offers a unique opportunity to explore the intersecting effects of interpatient, microenvironmental, and tumor cell heterogeneity on cell phenotypes in the TME. In this review, the current use of PDOs in complex 3D in vitro models of the TME is discussed and the emerging directions in the development of these models are highlighted. Next, work that has successfully applied single cell analysis to PDO-based models is examined and important experimental considerations are identified for this approach. Finally, open questions are highlighted that may be amenable to exploration using the integration of PDO-based models and single cell analysis. Ultimately, such investigations may facilitate the identification of novel therapeutic targets for cancer that address the significant influence of tumor-TME interactions.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Dynamic single-cell regulomes characterize human peripheral blood innate lymphoid cell subpopulations

Innate lymphoid cells (ILCs) are plastic immune cells divided into 3 main subsets, characterized by distinct phenotypic and functional profiles. Using single cell approaches, heightened heterogeneity of mouse ILCs has been appreciated, imprinted by tissue signals that shape their transcriptome and epigenome. Intra-subset diversity has also been observed in human ILCs. However, combined transcriptomic and epigenetic analyses of single ILCs in humans are lacking. Here, we show high transcriptional and epigenetic heterogeneity among human circulating ILCs in healthy individuals. We describe phenotypically distinct subclusters and diverse chromatin accessibility within main ILC populations, compatible with differentially poised states. We validate the use of this healthy donor-based analysis as resource dataset to help inferring ILC changes occurring in disease conditions. Overall, our work provides insights in the complex human ILC biology. We anticipate it to facilitate hypothesis-driven studies in patients, without the need to perform single cell OMICs using precious patients' material.

Cell-connectivity-guided trajectory inference from single-cell data

MOTIVATION

Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise.

RESULTS

We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge.

AVAILABILITY AND IMPLEMENTATION

Totem is available as an R package at https://github.com/elolab/Totem.

Mapping epigenetic modifications by sequencing technologies

The "epigenetics" concept was first described in 1942. Thus far, chemical modifications on histones, DNA, and RNA have emerged as three important building blocks of epigenetic modifications. Many epigenetic modifications have been intensively studied and found to be involved in most essential biological processes as well as human diseases, including cancer. Precisely and quantitatively mapping over 100 [1], 17 [2], and 160 [3] different known types of epigenetic modifications in histone, DNA, and RNA is the key to understanding the role of epigenetic modifications in gene regulation in diverse biological processes. With the rapid development of sequencing technologies, scientists are able to detect specific epigenetic modifications with various quantitative, high-resolution, whole-genome/transcriptome approaches. Here, we summarize recent advances in epigenetic modification sequencing technologies, focusing on major histone, DNA, and RNA modifications in mammalian cells.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Micropipette-assisted atomic force microscopy for single-cell 3D manipulations and nanomechanical measurements

Mechanical cues play a crucial role in regulating physiological and pathological processes, and atomic force microscopy (AFM) has become an important and standard tool for measuring the mechanical properties of single cells. In particular, providing a capability to manipulate cells in a three-dimensional (3D) space benefits enhancing the applications of AFM measurements in cell biology. Here, we present the complementary integration of AFM and micropipette micromanipulation, which allows precise 3D manipulations and nanomechanical measurements of single living cells. A micropipette micromanipulation system under the guidance of optical microscopy was established to isolate single living cells, and polydimethylsiloxane (PDMS) micropillar substrates were used to physically immobilize the isolated living cells for downstream AFM detection. The viscoelastic properties (Young's modulus, relaxation time, viscosity) of cells were quantitatively measured by AFM-based indentation assay. The effectiveness of micropipette-assisted AFM in single-cell analysis was confirmed on both living animal suspended cells and living animal adherent cells, showing dramatic changes in cell mechanics in different states and revealing the dynamics of single cells grown on micropillar arrays. The study demonstrates the great potential of a micropipette to aid AFM in single-cell manipulations for better accessing the mechanical cues involved in cellular processes, which will allow additional studies of single-cell mechanics and will benefit the field of mechanobiology.

Resolving the hematopoietic stem cell state by linking functional and molecular assays

One of the most challenging aspects of stem cell research is the reliance on retrospective assays for ascribing function. This is especially problematic for hematopoietic stem cell (HSC) research in which the current functional assay that formally establishes its HSC identity involves long-term serial transplantation assays that necessitate the destruction of the initial cell state many months before knowing that it was, in fact, an HSC. In combination with the explosion of equally destructive single-cell molecular assays, the paradox facing researchers is how to determine the molecular state of a functional HSC when you cannot concomitantly assess its functional and molecular properties. In this review, we will give a historical overview of the functional and molecular assays in the field, identify new tools that combine molecular and functional readouts in populations of HSCs, and imagine the next generation of computational and molecular profiling tools that may help us better link cell function with molecular state.

Genome annotation: From human genetics to biodiversity genomics

Within the next decade, the genomes of 1.8 million eukaryotic species will be sequenced. Identifying genes in these sequences is essential to understand the biology of the species. This is challenging due to the transcriptional complexity of eukaryotic genomes, which encode hundreds of thousands of transcripts of multiple types. Among these, a small set of protein-coding mRNAs play a disproportionately large role in defining phenotypes. Due to their sequence conservation, orthology can be established, making it possible to define the universal catalog of eukaryotic protein-coding genes. This catalog should substantially contribute to uncovering the genomic events underlying the emergence of eukaryotic phenotypes. This piece briefly reviews the basics of protein-coding gene prediction, discusses challenges in finalizing annotation of the human genome, and proposes strategies for producing annotations across the eukaryotic Tree of Life. This lays the groundwork for obtaining the catalog of all genes-the Earth's code of life.