Synthetic recording and in situ readout of lineage information in single cells

Visualization of individual cell division history in complex tissues using iCOUNT

The division potential of individual stem cells and the molecular consequences of successive rounds of proliferation remain largely unknown. Here, we developed an inducible cell division counter (iCOUNT) that reports cell division events in human and mouse tissues in vitro and in vivo. Analyzing cell division histories of neural stem/progenitor cells (NSPCs) in the developing and adult brain, we show that iCOUNT can provide novel insights into stem cell behavior. Further, we use single-cell RNA sequencing (scRNA-seq) of iCOUNT-labeled NSPCs and their progenies from the developing mouse cortex and forebrain-regionalized human organoids to identify functionally relevant molecular pathways that are commonly regulated between mouse and human cells, depending on individual cell division histories. Thus, we developed a tool to characterize the molecular consequences of repeated cell divisions of stem cells that allows an analysis of the cellular principles underlying tissue formation, homeostasis, and repair.

Advanced Technologies for Local Neural Circuits in the Cerebral Cortex

The neural network in the brain can be viewed as an integrated system assembled from a large number of local neural circuits specialized for particular brain functions. Activities of neurons in local neural circuits are thought to be organized both spatially and temporally under the rules optimized for their roles in information processing. It is well perceived that different areas of the mammalian neocortex have specific cognitive functions and distinct computational properties. However, the organizational principles of the local neural circuits in different cortical regions have not yet been clarified. Therefore, new research principles and related neuro-technologies that enable efficient and precise recording of large-scale neuronal activities and synaptic connections are necessary. Innovative technologies for structural analysis, including tissue clearing and expansion microscopy, have enabled super resolution imaging of the neural circuits containing thousands of neurons at a single synapse resolution. The imaging resolution and volume achieved by new technologies are beyond the limits of conventional light or electron microscopic methods. Progress in genome editing and related technologies has made it possible to label and manipulate specific cell types and discriminate activities of multiple cell types. These technologies will provide a breakthrough for multiscale analysis of the structure and function of local neural circuits. This review summarizes the basic concepts and practical applications of the emerging technologies and new insight into local neural circuits obtained by these technologies.

Get ready for the CRISPR/Cas system: A beginner's guide to the engineering and design of guide RNAs

The clustered regularly interspaced short palindromic repeats (CRISPR) system is a state-of-the-art tool for versatile genome editing that has advanced basic research dramatically, with great potential for clinic applications. The system consists of two key molecules: a CRISPR-associated (Cas) effector nuclease and a single guide RNA. The simplicity of the system has enabled the development of a wide spectrum of derivative methods. Almost any laboratory can utilize these methods, although new users may initially be confused when faced with the potentially overwhelming abundance of choices. Cas nucleases and their engineering have been systematically reviewed previously. In the present review, we discuss single guide RNA engineering and design strategies that facilitate more efficient, more specific and safer gene editing.

Spatial Organization of Chromatin: Emergence of Chromatin Structure During Development

Nuclei are central hubs for information processing in eukaryotic cells. The need to fit large genomes into small nuclei imposes severe restrictions on genome organization and the mechanisms that drive genome-wide regulatory processes. How a disordered polymer such as chromatin, which has vast heterogeneity in its DNA and histone modification profiles, folds into discernibly consistent patterns is a fundamental question in biology. Outstanding questions include how genomes are spatially and temporally organized to regulate cellular processes with high precision and whether genome organization is causally linked to transcription regulation. The advent of next-generation sequencing, super-resolution imaging, multiplexed fluorescent in situ hybridization, and single-molecule imaging in individual living cells has caused a resurgence in efforts to understand the spatiotemporal organization of the genome. In this review, we discuss structural and mechanistic properties of genome organization at different length scales and examine changes in higher-order chromatin organization during important developmental transitions.

Decoding and recoding plant development

The development of multicellular organisms has been studied for centuries, yet many critical events and mechanisms of regulation remain challenging to observe directly. Early research focused on detailed observational and comparative studies. Molecular biology has generated insights into regulatory mechanisms, but only for a limited number of species. Now, synthetic biology is bringing these two approaches together, and by adding the possibility of sculpting novel morphologies, opening another path to understanding biology. Here, we review a variety of recently invented techniques that use CRISPR/Cas9 and phage integrases to trace the differentiation of cells over various timescales, as well as to decode the molecular states of cells in high spatiotemporal resolution. Most of these tools have been implemented in animals. The time is ripe for plant biologists to adopt and expand these approaches. Here, we describe how these tools could be used to monitor development in diverse plant species, as well as how they could guide efforts to recode programs of interest.

Defining cell types and lineage in the Drosophila midgut using single cell transcriptomics

The Drosophila midgut has emerged in recent years as a model system to study stem cell renewal and differentiation and tissue homeostasis. Histological, genetic and gene expression studies have provided a wealth of information on gut cell types, regionalization, genes and pathways involved in cell proliferation and differentiation, stem cell renewal, and responses to changes in environmental factors such as the microbiota and nutrients. Here, we review the contribution of single cell transcriptomic methods to our understanding of gut cell type diversity, lineage and behavior.

Nuclear compartmentalization as a mechanism of quantitative control of gene expression

Gene regulation requires the dynamic coordination of hundreds of regulatory factors at precise genomic and RNA targets. Although many regulatory factors have specific affinity for their nucleic acid targets, molecular diffusion and affinity models alone cannot explain many of the quantitative features of gene regulation in the nucleus. One emerging explanation for these quantitative properties is that DNA, RNA and proteins organize within precise, 3D compartments in the nucleus to concentrate groups of functionally related molecules. Recently, nucleic acids and proteins involved in many important nuclear processes have been shown to engage in cooperative interactions, which lead to the formation of condensates that partition the nucleus. In this Review, we discuss an emerging perspective of gene regulation, which moves away from classic models of stoichiometric interactions towards an understanding of how spatial compartmentalization can lead to non-stoichiometric molecular interactions and non-linear regulatory behaviours. We describe key mechanisms of nuclear compartment formation, including emerging roles for non-coding RNAs in facilitating their formation, and discuss the functional role of nuclear compartments in transcription regulation, co-transcriptional and post-transcriptional RNA processing, and higher-order chromatin regulation. More generally, we discuss how compartmentalization may explain important quantitative aspects of gene regulation.

Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states

Single-cell RNA sequencing has revealed extensive transcriptional cell state diversity in cancer, often observed independently of genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, here we performed multiomics single-cell profiling-integrating DNA methylation, transcriptome and genotype within the same cells-of diffuse gliomas, tumors characterized by defined transcriptional cell state diversity. Direct comparison of the epigenetic profiles of distinct cell states revealed key switches for state transitions recapitulating neurodevelopmental trajectories and highlighted dysregulated epigenetic mechanisms underlying gliomagenesis. We further developed a quantitative framework to directly measure cell state heritability and transition dynamics based on high-resolution lineage trees in human samples. We demonstrated heritability of malignant cell states, with key differences in hierarchal and plastic cell state architectures in IDH-mutant glioma versus IDH-wild-type glioblastoma, respectively. This work provides a framework anchoring transcriptional cancer cell states in their epigenetic encoding, inheritance and transition dynamics.

Current approaches to fate mapping and lineage tracing using image data

Visualizing, tracking and reconstructing cell lineages in developing embryos has been an ongoing effort for well over a century. Recent advances in light microscopy, labelling strategies and computational methods to analyse complex image datasets have enabled detailed investigations into the fates of cells. Combined with powerful new advances in genomics and single-cell transcriptomics, the field of developmental biology is able to describe the formation of the embryo like never before. In this Review, we discuss some of the different strategies and applications to lineage tracing in live-imaging data and outline software methodologies that can be applied to various cell-tracking challenges.

Applications of high-resolution clone tracking technologies in cancer

Tumors are comprised of dynamic, heterogenous cell populations characterized by numerous genetic and non-genetic alterations that accumulate and change with disease progression and treatment. Retrospective analyses of tumor evolution have relied on the measurement of genetic markers (such as copy number variants) to infer clonal dynamics. However, these approaches neglect the critical contributions of non-genetic drivers of disease. Techniques that harness the power of prospective clone tracking via heritable barcode tags provide an alternative strategy. In this review, we discuss methods for high-resolution, quantitative clone tracking, including recent advancements to pair barcode-specific functionality with scRNA-seq, clonal cell isolation, and in situ hybridization and imaging. We discuss these approaches in the context of cancer cell heterogeneity and treatment resistance.

Applications of Single-Cell DNA Sequencing

Over the past decade, genomic analyses of single cells-the fundamental units of life-have become possible. Single-cell DNA sequencing has shed light on biological questions that were previously inaccessible across diverse fields of research, including somatic mutagenesis, organismal development, genome function, and microbiology. Single-cell DNA sequencing also promises significant future biomedical and clinical impact, spanning oncology, fertility, and beyond. While single-cell approaches that profile RNA and protein have greatly expanded our understanding of cellular diversity, many fundamental questions in biology and important biomedical applications require analysis of the DNA of single cells. Here, we review the applications and biological questions for which single-cell DNA sequencing is uniquely suited or required. We include a discussion of the fields that will be impacted by single-cell DNA sequencing as the technology continues to advance.

Single-cell analysis of cell fate bifurcation in the chordate Ciona

BACKGROUND

Inductive signaling interactions between different cell types are a major mechanism for the further diversification of embryonic cell fates. Most blastomeres in the model chordate Ciona robusta become restricted to a single predominant fate between the 64-cell and mid-gastrula stages. The deeply stereotyped and well-characterized Ciona embryonic cell lineages allow the transcriptomic analysis of newly established cell types very early in their divergence from sibling cell states without the pseudotime inference needed in the analysis of less synchronized cell populations. This is the first ascidian study to use droplet scRNAseq with large numbers of analyzed cells as early as the 64-cell stage when major lineages such as primary notochord first become fate restricted.

RESULTS AND CONCLUSIONS

We identify 59 distinct cell states, including new subregions of the b-line neural lineage and the early induction of the tail tip epidermis. We find that 34 of these cell states are directly or indirectly dependent on MAPK-mediated signaling critical to early Ciona patterning. Most of the MAPK-dependent bifurcations are canalized with the signal-induced cell fate lost upon MAPK inhibition, but the posterior endoderm is unique in being transformed into a novel state expressing some but not all markers of both endoderm and muscle. Divergent gene expression between newly bifurcated sibling cell types is dominated by upregulation in the induced cell type. The Ets family transcription factor Elk1/3/4 is uniquely upregulated in nearly all the putatively direct inductions. Elk1/3/4 upregulation together with Ets transcription factor binding site enrichment analysis enables inferences about which bifurcations are directly versus indirectly controlled by MAPK signaling. We examine notochord induction in detail and find that the transition between a Zic/Ets-mediated regulatory state and a Brachyury/FoxA-mediated regulatory state is unexpectedly late. This supports a "broad-hourglass" model of cell fate specification in which many early tissue-specific genes are induced in parallel to key tissue-specific transcriptional regulators via the same set of transcriptional inputs.

A multiscale analysis of early flower development in Arabidopsis provides an integrated view of molecular regulation and growth control

We have analyzed the link between the gene regulation and growth during the early stages of flower development in Arabidopsis. Starting from time-lapse images, we generated a 4D atlas of early flower development, including cell lineage, cellular growth rates, and the expression patterns of regulatory genes. This information was introduced in MorphoNet, a web-based platform. Using computational models, we found that the literature-based molecular network only explained a minority of the gene expression patterns. This was substantially improved by adding regulatory hypotheses for individual genes. Correlating growth with the combinatorial expression of multiple regulators led to a set of hypotheses for the action of individual genes in morphogenesis. This identified the central factor LEAFY as a potential regulator of heterogeneous growth, which was supported by quantifying growth patterns in a leafy mutant. By providing an integrated view, this atlas should represent a fundamental step toward mechanistic models of flower development.

Gene expression at a single-molecule level: implications for myelodysplastic syndromes and acute myeloid leukemia

Nongenetic heterogeneity, or gene expression stochasticity, is an important source of variability in biological systems. With the advent and improvement of single molecule resolution technologies, it has been shown that transcription dynamics and resultant transcript number fluctuations generate significant cell-to-cell variability that has important biological effects and may contribute substantially to both tissue homeostasis and disease. In this respect, the pathophysiology of stem cell-derived malignancies such as acute myeloid leukemia and myelodysplastic syndromes, which has historically been studied at the ensemble level, may require reevaluation. To that end, it is our aim in this review to highlight the results of recent single-molecule, biophysical, and systems studies of gene expression dynamics, with the explicit purpose of demonstrating how the insights from these basic science studies may help inform and progress the field of leukemia biology and, ultimately, research into novel therapies.

A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing

Lineage reconstruction is central to understanding tissue development and maintenance. To overcome the limitations of current techniques that typically reconstruct clonal trees using genetically encoded reporters, we report scPECLR, a probabilistic algorithm to endogenously infer lineage trees at a single-cell-division resolution by using 5-hydroxymethylcytosine (5hmC). When applied to 8-cell pre-implantation mouse embryos, scPECLR predicts the full lineage tree with greater than 95% accuracy. In addition, we developed scH&G-seq to sequence both 5hmC and genomic DNA from the same cell. Given that genomic DNA sequencing yields information on both copy number variations and single-nucleotide polymorphisms, when combined with scPECLR it enables more accurate lineage reconstruction of larger trees. Finally, we show that scPECLR can also be used to map chromosome strand segregation patterns during cell division, thereby providing a strategy to test the "immortal strand" hypothesis. Thus, scPECLR provides a generalized method to endogenously reconstruct lineage trees at an individual-cell-division resolution.

Unraveling Tumor Heterogeneity by Using DNA Barcoding Technologies to Develop Personalized Treatment Strategies in Advanced-Stage PDAC

Tumor heterogeneity is a hallmark of many solid tumors, including pancreatic ductal adenocarcinoma (PDAC), and an inherent consequence of the clonal evolution of cancers. As such, it is considered the underlying concept of many characteristics of the disease, including the ability to metastasize, adapt to different microenvironments, and to develop therapy resistance. Undoubtedly, the high mortality of PDAC can be attributed to a high extent to these properties. Despite its apparent importance, studying tumor heterogeneity has been a challenging task, mainly due to its complexity and lack of appropriate methods. However, in recent years molecular DNA barcoding has emerged as a sophisticated tool that allows mapping of individual cells or subpopulations in a cell pool to study heterogeneity and thus devise new personalized treatment strategies. In this review, we provide an overview of genetic and non-genetic inter- and intra-tumor heterogeneity and its impact on (personalized) treatment strategies in PDAC and address how DNA barcoding technologies work and can be applied to study this clinically highly relevant question.

Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees

The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.

Non-coding Natural Antisense Transcripts: Analysis and Application

Non-coding natural antisense transcripts (ncNATs) are regulatory RNA sequences that are transcribed in the opposite direction to protein-coding or non-coding transcripts. These transcripts are implicated in a broad variety of biological and pathological processes, including tumorigenesis and oncogenic progression. With this complex field still in its infancy, annotations, expression profiling and functional characterisations of ncNATs are far less comprehensive than those for protein-coding genes, pointing out substantial gaps in the analysis and characterisation of these regulatory transcripts. In this review, we discuss ncNATs from an analysis perspective, in particular regarding the use of high-throughput sequencing strategies, such as RNA-sequencing, and summarize the unique challenges of investigating the antisense transcriptome. Finally, we elaborate on their potential as biomarkers and future targets for treatment, focusing on cancer.

Synthetic gene circuits as tools for drug discovery

Within mammalian systems, there exists enormous opportunity to use synthetic gene circuits to enhance phenotype-based drug discovery, to map the molecular origins of disease, and to validate therapeutics in complex cellular systems. While drug discovery has relied on marker staining and high-content imaging in cell-based assays, synthetic gene circuits expand the potential for precision and speed. Here we present a vision of how circuits can improve the speed and accuracy of drug discovery by enhancing the efficiency of hit triage, capturing disease-relevant dynamics in cell-based assays, and simplifying validation and readouts from organoids and microphysiological systems (MPS). By tracking events and cellular states across multiple length and time scales, circuits will transform how we decipher the causal link between molecular events and phenotypes to improve the selectivity and sensitivity of cell-based assays.

Spatial omics and multiplexed imaging to explore cancer biology

Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

Exploring tissue architecture using spatial transcriptomics

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

John Sulston (1942-2018): a personal perspective

John Sulston changed the way we do science, not once, but three times - initially with the complete cell lineage of the nematode Caenorhabditis elegans, next with completion of the genome sequences of the worm and human genomes and finally with his strong and active advocacy for open data sharing. His contributions were widely recognized and in 2002 he received the Nobel Prize in Physiology and Medicine.

Retrospective cell lineage reconstruction in humans by using short tandem repeats

Cell lineage analysis aims to uncover the developmental history of an organism back to its cell of origin. Recently, novel in vivo methods utilizing genome editing enabled important insights into the cell lineages of animals. In contrast, human cell lineage remains restricted to retrospective approaches, which still lack resolution and cost-efficient solutions. Here, we demonstrate a scalable platform based on short tandem repeats targeted by duplex molecular inversion probes. With this human cell lineage tracing method, we accurately reproduced a known lineage of DU145 cells and reconstructed lineages of healthy and metastatic single cells from a melanoma patient who matched the anatomical reference while adding further refinements. This platform allowed us to faithfully recapitulate lineages of developmental tissue formation in healthy cells. In summary, our lineage discovery platform can profile informative somatic mutations efficiently and provides solid lineage reconstructions even in challenging low-mutation-rate healthy single cells.

Variability within rare cell states enables multiple paths toward drug resistance

Molecular differences between individual cells can lead to dramatic differences in cell fate, such as death versus survival of cancer cells upon drug treatment. These originating differences remain largely hidden due to difficulties in determining precisely what variable molecular features lead to which cellular fates. Thus, we developed Rewind, a methodology that combines genetic barcoding with RNA fluorescence in situ hybridization to directly capture rare cells that give rise to cellular behaviors of interest. Applying Rewind to BRAFV600E melanoma, we trace drug-resistant cell fates back to single-cell gene expression differences in their drug-naive precursors (initial frequency of ~1:1,000-1:10,000 cells) and relative persistence of MAP kinase signaling soon after drug treatment. Within this rare subpopulation, we uncover a rich substructure in which molecular differences among several distinct subpopulations predict future differences in phenotypic behavior, such as proliferative capacity of distinct resistant clones after drug treatment. Our results reveal hidden, rare-cell variability that underlies a range of latent phenotypic outcomes upon drug exposure.

Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.

Lineage tracing and analog recording in mammalian cells by single-site DNA writing

Studying cellular and developmental processes in complex multicellular organisms can require the non-destructive observation of thousands to billions of cells deep within an animal. DNA recorders address the staggering difficulty of this task by converting transient cellular experiences into mutations at defined genomic sites that can be sequenced later in high throughput. However, existing recorders act primarily by erasing DNA. This is problematic because, in the limit of progressive erasure, no record remains. We present a DNA recorder called CHYRON (Cell History Recording by Ordered Insertion) that acts primarily by writing new DNA through the repeated insertion of random nucleotides at a single locus in temporal order. To achieve in vivo DNA writing, CHYRON combines Cas9, a homing guide RNA and the template-independent DNA polymerase terminal deoxynucleotidyl transferase. We successfully applied CHYRON as an evolving lineage tracer and as a recorder of user-selected cellular stimuli.

Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

The Intestinal Epithelium - Fluid Fate and Rigid Structure From Crypt Bottom to Villus Tip

The single-layered, simple epithelium of the gastro-intestinal tract controls nutrient uptake, coordinates our metabolism and shields us from pathogens. Despite its seemingly simple architecture, the intestinal lining consists of highly distinct cell populations that are continuously renewed by the same stem cell population. The need to maintain balanced diversity of cell types in an unceasingly regenerating tissue demands intricate mechanisms of spatial or temporal cell fate control. Recent advances in single-cell sequencing, spatio-temporal profiling and organoid technology have shed new light on the intricate micro-structure of the intestinal epithelium and on the mechanisms that maintain it. This led to the discovery of unexpected plasticity, zonation along the crypt-villus axis and new mechanism of self-organization. However, not only the epithelium, but also the underlying mesenchyme is distinctly structured. Several new studies have explored the intestinal stroma with single cell resolution and unveiled important interactions with the epithelium that are crucial for intestinal function and regeneration. In this review, we will discuss these recent findings and highlight the technologies that lead to their discovery. We will examine strengths and limitations of each approach and consider the wider impact of these results on our understanding of the intestine in health and disease.

Spatial molecular profiling: platforms, applications and analysis tools

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

CRISPR-Cas Tools and Their Application in Genetic Engineering of Human Stem Cells and Organoids

CRISPR-Cas technology has revolutionized biological research and holds great therapeutic potential. Here, we review CRISPR-Cas systems and their latest developments with an emphasis on application to human cells. We also discuss how different CRISPR-based strategies can be used to accomplish a particular genome engineering goal. We then review how different CRISPR tools have been used in genome engineering of human stem cells in vitro, covering both the pluripotent (iPSC/ESC) and somatic adult stem cell fields and, in particular, 3D organoid cultures. Finally, we discuss the progress and challenges associated with CRISPR-based genome editing of human stem cells for therapeutic use.

Bridging scales: From cell biology to physiology using in situ single-cell technologies

Biological organization crosses multiple spatial scales: from molecular, cellular, to tissues and organs. The proliferation of molecular profiling technologies enables increasingly detailed cataloging of the components at each scale. However, the scarcity of spatial profiling has made it challenging to bridge across these scales. Emerging technologies based on highly multiplexed in situ profiling are paving the way to study the spatial organization of cells and tissues in greater detail. These new technologies provide the data needed to cross the scale from cell biology to physiology and identify the fundamental principles that govern tissue organization. Here, we provide an overview of these key technologies and discuss the current and future insights these powerful techniques enable.

Cryptic surface-associated multicellularity emerges through cell adhesion and its regulation

The repeated evolution of multicellularity led to a wide diversity of organisms, many of which are sessile, including land plants, many fungi, and colonial animals. Sessile organisms adhere to a surface for most of their lives, where they grow and compete for space. Despite the prevalence of surface-associated multicellularity, little is known about its evolutionary origin. Here, we introduce a novel theoretical approach, based on spatial lineage tracking of cells, to study this origin. We show that multicellularity can rapidly evolve from two widespread cellular properties: cell adhesion and the regulatory control of adhesion. By evolving adhesion, cells attach to a surface, where they spontaneously give rise to primitive cell collectives that differ in size, life span, and mode of propagation. Selection in favor of large collectives increases the fraction of adhesive cells until a surface becomes fully occupied. Through kin recognition, collectives then evolve a central-peripheral polarity in cell adhesion that supports a division of labor between cells and profoundly impacts growth. Despite this spatial organization, nascent collectives remain cryptic, lack well-defined boundaries, and would require experimental lineage tracking technologies for their identification. Our results suggest that cryptic multicellularity could readily evolve and originate well before multicellular individuals become morphologically evident.

Bayesian inference of gene expression states from single-cell RNA-seq data

Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data.

How should we define mammary stem cells?

Mammary stem cells (MaSCs) have been defined by cell surface marker expression and their ability to repopulate a cleared fat pad, a capacity now known to result from reprogramming upon transplantation. Furthermore, lineage-tracing studies have provoked controversy as to whether MaSCs are unipotent or bi/multipotent. Various innovative experimental approaches, including single-cell RNA sequencing (scRNA-Seq), epigenetic analyses, deep tissue and live imaging, and advanced mouse models, have provided new and unexpected insights into stem and progenitor cells; thus, it is now timely to reappraise our concept of the MaSC hierarchy. Here, I highlight misconceptions, suggest definitions of stem and progenitor cells, and propose a way forward in our search for an understanding of MaSCs.

Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.

Imaging cell lineage with a synthetic digital recording system

During multicellular development, spatial position and lineage history play powerful roles in controlling cell fate decisions. Using a serine integrase-based recording system, we engineered cells to record lineage information in a format that can be read out in situ. The system, termed integrase-editable memory by engineered mutagenesis with optical in situ readout (intMEMOIR), allowed in situ reconstruction of lineage relationships in cultured mouse cells and flies. intMEMOIR uses an array of independent three-state genetic memory elements that can recombine stochastically and irreversibly, allowing up to 59,049 distinct digital states. It reconstructed lineage trees in stem cells and enabled simultaneous analysis of single-cell clonal history, spatial position, and gene expression in Drosophila brain sections. These results establish a foundation for microscopy-readable lineage recording and analysis in diverse systems.

Lineage barcoding in mice with homing CRISPR

Classic approaches to mapping the developmental history of cells in vivo have relied on techniques that require complex interventions and often capture only a single trajectory or moment in time. We have previously described a developmental barcoding system to address these issues using synthetically induced mutations to record information about each cell's lineage in its genome. This system uses MARC1 mouse lines, which have multiple homing guide RNAs that each generate hundreds of mutant alleles and combine to produce an exponential diversity of barcodes. Here, we detail two MARC1 lines that are available from a public repository. We describe strategies for using MARC1 mice and experimental design considerations. We provide a protocol for barcode retrieval and sequencing as well as the analysis of the sequencing data. This protocol generates barcodes based on synthetically induced mutations in mice to enable lineage analysis.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Taming human brain organoids one cell at a time

Human brain organoids are self-organizing three-dimensional structures that emerge from human pluripotent stem cells and mimic aspects of the cellular composition and functionality of the developing human brain. Despite their impressive self-organizing capacity, organoids lack the stereotypic structural anatomy of their in vivo counterpart, making conventional analysis techniques underpowered to assess cellular composition and gene network regulation in organoids. Advances in single cell transcriptomics have recently allowed characterization and improvement of organoid protocols, as they continue to evolve, by enabling identification of cell types and states along with their developmental origins. In this review, we summarize recent approaches, progresses and challenges in resolving brain organoid's complexity through single-cell transcriptomics. We then discuss emerging technologies that may complement single-cell RNA sequencing by providing additional readouts of cellular states to generate an organ-level view of developmental processes. Altogether, these integrative technologies will allow monitoring of global gene regulation in thousands of individual cells and will offer an unprecedented opportunity to investigate features of human brain development and disease across multiple cellular modalities and with cell-type resolution.

RNA timestamps identify the age of single molecules in RNA sequencing

Current approaches to single-cell RNA sequencing (RNA-seq) provide only limited information about the dynamics of gene expression. Here we present RNA timestamps, a method for inferring the age of individual RNAs in RNA-seq data by exploiting RNA editing. To introduce timestamps, we tag RNA with a reporter motif consisting of multiple MS2 binding sites that recruit the adenosine deaminase ADAR2 fused to an MS2 capsid protein. ADAR2 binding to tagged RNA causes A-to-I edits to accumulate over time, allowing the age of the RNA to be inferred with hour-scale accuracy. By combining observations of multiple timestamped RNAs driven by the same promoter, we can determine when the promoter was active. We demonstrate that the system can infer the presence and timing of multiple past transcriptional events. Finally, we apply the method to cluster single cells according to the timing of past transcriptional activity. RNA timestamps will allow the incorporation of temporal information into RNA-seq workflows.

Deciphering neural heterogeneity through cell lineage tracing

Understanding how an adult brain reaches an appropriate size and cell composition from a pool of progenitors that proliferates and differentiates is a key question in Developmental Neurobiology. Not only the control of final size but also, the proper arrangement of cells of different embryonic origins is fundamental in this process. Each neural progenitor has to produce a precise number of sibling cells that establish clones, and all these clones will come together to form the functional adult nervous system. Lineage cell tracing is a complex and challenging process that aims to reconstruct the offspring that arise from a single progenitor cell. This tracing can be achieved through strategies based on genetically modified organisms, using either genetic tracers, transfected viral vectors or DNA constructs, and even single-cell sequencing. Combining different reporter proteins and the use of transgenic mice revolutionized clonal analysis more than a decade ago and now, the availability of novel genome editing tools and single-cell sequencing techniques has vastly improved the capacity of lineage tracing to decipher progenitor potential. This review brings together the strategies used to study cell lineages in the brain and the role they have played in our understanding of the functional clonal relationships among neural cells. In addition, future perspectives regarding the study of cell heterogeneity and the ontogeny of different cell lineages will also be addressed.

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17 We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.

Recording of elapsed time and temporal information about biological events using Cas9

DNA has not been utilized to record temporal information, although DNA has been used to record biological information and to compute mathematical problems. Here, we found that indel generation by Cas9 and guide RNA can occur at steady rates, in contrast to typical dynamic biological reactions, and the accumulated indel frequency can be a function of time. By measuring indel frequencies, we developed a method for recording and measuring absolute time periods over hours to weeks in mammalian cells. These time-recordings were conducted in several cell types, with different promoters and delivery vectors for Cas9, and in both cultured cells and cells of living mice. As applications, we recorded the duration of chemical exposure and the lengths of elapsed time since the onset of biological events (e.g., heat exposure and inflammation). We propose that our systems could serve as synthetic "DNA clocks."

Phylodynamics for cell biologists

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

Sequential Activation of Guide RNAs to Enable Successive CRISPR-Cas9 Activities

Currently, either highly multiplexed genetic manipulations can be delivered to mammalian cells all at once or extensive engineering of gene regulatory sequences can be used to conditionally activate a few manipulations. Here, we provide proof of principle for a new system enabling multiple genetic manipulations to be executed as a preprogrammed cascade of events. The system leverages the programmability of the S. pyogenes Cas9 and is based on flexible arrangements of individual modules of activity. The basic module consists of an inactive single-guide RNA (sgRNA)-like component that is converted to an active state through the effects of another sgRNA. Modules can be arranged to bring about an algorithmic program of sequential genetic manipulations without the need for engineering cell-type-specific promoters or gene regulatory sequences. With the expanding diversity of available tools that use spCas9, this sgRNA-based system provides multiple levels of interfacing with mammalian cell biology.

Full-Spectrum Targeted Mutagenesis in Plant and Animal Cells

Directed evolution is a powerful approach for protein engineering and functional studies. However, directed evolution outputs from bacterial and yeast systems do not always translate to higher organisms. In situ directed evolution in plant and animal cells has previously been limited by an inability to introduce targeted DNA sequence diversity. New hypermutation tools have emerged that can generate targeted mutations in plant and animal cells, by recruiting mutagenic proteins to defined DNA loci. Progress in this field, such as the development of CRISPR-derived hypermutators, now allows for all DNA nucleotides within user-defined regions to be altered through the recruitment of error-prone DNA polymerases or highly active DNA deaminases. The further engineering of these mutagenesis systems will potentially allow for all transition and transversion substitutions to be generated within user-defined genomic windows. Such targeted full-spectrum mutagenesis tools would provide a powerful platform for evolving antibodies, enzymes, structural proteins and RNAs with specific desired properties in relevant cellular contexts. These tools are expected to benefit many aspects of biological research and, ultimately, clinical applications.

How stem cells remember their past

Somatic stem cells are required for tissue development, homeostasis, and repair. Recent data suggested that previous biographical experiences of individual stem cells influence their behavior in the context of tissue formation and govern stem cell responses to external stimuli. Here we provide a concise review how a cell's biography, for example, previous rounds of cell divisions or the age-dependent accumulation of cellular damage, is remembered in stem cells and how previous experiences affect the segregation of cellular components, thus guiding cellular behavior in vertebrate stem cells. Further, we suggest future directions of research that may help to unravel the molecular underpinnings of how past experiences guide future cellular behavior.

Computational Stem Cell Biology: Open Questions and Guiding Principles

Computational biology is enabling an explosive growth in our understanding of stem cells and our ability to use them for disease modeling, regenerative medicine, and drug discovery. We discuss four topics that exemplify applications of computation to stem cell biology: cell typing, lineage tracing, trajectory inference, and regulatory networks. We use these examples to articulate principles that have guided computational biology broadly and call for renewed attention to these principles as computation becomes increasingly important in stem cell biology. We also discuss important challenges for this field with the hope that it will inspire more to join this exciting area.

Single-cell genomics to understand disease pathogenesis

Cells are minimal functional units in biological phenomena, and therefore single-cell analysis is needed to understand the molecular behavior leading to cellular function in organisms. In addition, omics analysis technology can be used to identify essential molecular mechanisms in an unbiased manner. Recently, single-cell genomics has unveiled hidden molecular systems leading to disease pathogenesis in patients. In this review, I summarize the recent advances in single-cell genomics for the understanding of disease pathogenesis and discuss future perspectives.