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

MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial omics data in bioconductor

MOTIVATION

Imaging-based spatial transcriptomics (ST) technologies have achieved subcellular resolution, enabling detection of individual molecules in their native tissue context. Data associated with these technologies promise unprecedented opportunity toward understanding cellular and subcellular biology. However, in R/Bioconductor, there is a scarcity of existing computational infrastructure to represent such data, and particularly to summarize and transform it for existing widely adopted computational tools in single-cell transcriptomics analysis, including SingleCellExperiment and SpatialExperiment (SPE) classes. With the emergence of several commercial offerings of imaging-based ST, there is a pressing need to develop consistent data structure standards for these technologies at the individual molecule-level.

RESULTS

To this end, we have developed MoleculeExperiment, an R/Bioconductor package, which (i) stores molecule and cell segmentation boundary information at the molecule-level, (ii) standardizes this molecule-level information across different imaging-based ST technologies, including 10× Genomics' Xenium, and (iii) streamlines transition from a MoleculeExperiment object to a SpatialExperiment object. Overall, MoleculeExperiment is generally applicable as a data infrastructure class for consistent analysis of molecule-resolved spatial omics data.

AVAILABILITY AND IMPLEMENTATION

The MoleculeExperiment package is publicly available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html. Source code is available on Github at: https://github.com/SydneyBioX/MoleculeExperiment. The vignette for MoleculeExperiment can be found at https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html.

Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes

Tracking cells as they divide and progress through differentiation is a fundamental step in understanding many biological processes, such as the development of organisms and progression of diseases. In this study, we investigate a machine learning approach to reconstruct lineage trees in experimental systems based on mutating synthetic genomic barcodes. We refine previously proposed methodology by embedding information of higher level relationships between cells and single-cell barcode values into a feature space. We test performance of the algorithm on shallow trees (up to 100 cells) and deep trees (up to 10 000 cells). Our proposed algorithm can improve tree reconstruction accuracy in comparison to reconstructions based on a maximum parsimony method, but this comes at a higher computational time requirement.

Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells

Even among genetically identical cancer cells, resistance to therapy frequently emerges from a small subset of those cells1-7. Molecular differences in rare individual cells in the initial population enable certain cells to become resistant to therapy7-9; however, comparatively little is known about the variability in the resistance outcomes. Here we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing, to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically and functionally distinct resistant types. These resistant types are largely predetermined by molecular differences between cells before drug addition and not by extrinsic factors. Changes in the dose and type of drug can switch the resistant type of an initial cell, resulting in the generation and elimination of certain resistant types. Samples from patients show evidence for the existence of these resistant types in a clinical context. We observed diversity in resistant types across several single-cell-derived cancer cell lines and cell types treated with a variety of drugs. The diversity of resistant types as a result of the variability in intrinsic cell states may be a generic feature of responses to external cues.

Engineered bacterial swarm patterns as spatial records of environmental inputs

A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated and rapid movement of bacteria powered by flagella. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye swarm patterns, to 'write' external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we separately show that growing colonies can record dynamic environmental changes. We decode the resulting multicondition patterns with deep classification and segmentation models. Finally, we engineer a strain that records the presence of aqueous copper. This work creates an approach for building macroscale bacterial recorders, expanding the framework for engineering emergent microbial behaviors.

Multiplex Protein Imaging through PACIFIC: Photoactive Immunofluorescence with Iterative Cleavage

Multiplex protein imaging technologies enable deep phenotyping and provide rich spatial information about biological samples. Existing methods have shown great success but also harbored trade-offs between various pros and cons, underscoring the persisting necessity to expand the imaging toolkits. Here we present PACIFIC: photoactive immunofluorescence with iterative cleavage, a new modality of multiplex protein imaging methods. PACIFIC achieves iterative multiplexing by implementing photocleavable fluorophores for antibody labeling with one-step spin-column purification. PACIFIC requires no specialized instrument, no DNA encoding, or chemical treatments. We demonstrate that PACIFIC can resolve cellular heterogeneity in both formalin-fixed paraffin-embedded (FFPE) samples and fixed cells. To further highlight how PACIFIC assists discovery, we integrate PACIFIC with live-cell tracking and identify phosphor-p70S6K as a critical driver that governs U87 cell mobility. Considering the cost, flexibility, and compatibility, we foresee that PACIFIC can confer deep phenotyping capabilities to anyone with access to traditional immunofluorescence platforms.

The expanding vistas of spatial transcriptomics

The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput. As such, they have enabled the determination of the cell-type architecture of tissues, the querying of cell-cell interactions and the monitoring of molecular interactions between tissue components. The rapidly evolving spatial genomics landscape will enable generalized high-throughput genomic measurements and perturbations to be performed in the context of tissues. These advances will empower hypothesis generation and biological discovery and bridge the worlds of tissue biology and genomics.

Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes

Random DNA barcodes are a versatile tool for tracking cell lineages, with applications ranging from development to cancer to evolution. Here, we review and critically evaluate barcode designs as well as methods of barcode sequencing and initial processing of barcode data. We first demonstrate how various barcode design decisions affect data quality and propose a new design that balances all considerations that we are currently aware of. We then discuss various options for the preparation of barcode sequencing libraries, including inline indices and Unique Molecular Identifiers (UMIs). Finally, we test the performance of several established and new bioinformatic pipelines for the extraction of barcodes from raw sequencing reads and for error correction. We find that both alignment and regular expression-based approaches work well for barcode extraction, and that error-correction pipelines designed specifically for barcode data are superior to generic ones. Overall, this review will help researchers to approach their barcoding experiments in a deliberate and systematic way.

Synthetic developmental biology: New tools to deconstruct and rebuild developmental systems

Technological advances have driven many recent advances in developmental biology. Light sheet imaging can reveal single-cell dynamics in living three-dimensional tissues, whereas single-cell genomic methods open the door to a complete catalogue of cell types and gene expression states. An equally powerful but complementary set of approaches are also becoming available to define development processes from the bottom up. These synthetic approaches aim to reconstruct the minimal developmental patterns, signaling processes, and gene networks that produce the basic set of developmental operations: spatial polarization, morphogen interpretation, tissue movement, and cellular memory. In this review we discuss recent approaches at the intersection of synthetic biology and development, including synthetic circuits to deliver and record signaling stimuli and synthetic reconstitution of pattern formation on multicellular scales.

Toward a systems-level probing of tumor clonality

Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.

CRISPR for neuroscientists

Genome engineering technologies provide an entry point into understanding and controlling the function of genetic elements in health and disease. The discovery and development of the microbial defense system CRISPR-Cas yielded a treasure trove of genome engineering technologies and revolutionized the biomedical sciences. Comprising diverse RNA-guided enzymes and effector proteins that evolved or were engineered to manipulate nucleic acids and cellular processes, the CRISPR toolbox provides precise control over biology. Virtually all biological systems are amenable to genome engineering-from cancer cells to the brains of model organisms to human patients-galvanizing research and innovation and giving rise to fundamental insights into health and powerful strategies for detecting and correcting disease. In the field of neuroscience, these tools are being leveraged across a wide range of applications, including engineering traditional and non-traditional transgenic animal models, modeling disease, testing genomic therapies, unbiased screening, programming cell states, and recording cellular lineages and other biological processes. In this primer, we describe the development and applications of CRISPR technologies while highlighting outstanding limitations and opportunities.

Recording of cellular physiological histories along optically readable self-assembling protein chains

Observing cellular physiological histories is key to understanding normal and disease-related processes. Here we describe expression recording islands-a fully genetically encoded approach that enables both continual digital recording of biological information within cells and subsequent high-throughput readout in fixed cells. The information is stored in growing intracellular protein chains made of self-assembling subunits, human-designed filament-forming proteins bearing different epitope tags that each correspond to a different cellular state or function (for example, gene expression downstream of neural activity or pharmacological exposure), allowing the physiological history to be read out along the ordered subunits of protein chains with conventional optical microscopy. We use expression recording islands to record gene expression timecourse downstream of specific pharmacological and physiological stimuli in cultured neurons and in living mouse brain, with a time resolution of a fraction of a day, over periods of days to weeks.

Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity

Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample.

Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios.

Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases.

Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.

Single-Cell Molecular Barcoding to Decode Multimodal Information Defining Cell States

Single-cell research has provided a breakthrough in biology to understand heterogeneous cell groups, such as tissues and organs, in development and disease. Molecular barcoding and subsequent sequencing technology insert a singlecell barcode into isolated single cells, allowing separation cell by cell. Given that multimodal information from a cell defines precise cellular states, recent technical advances in methods focus on simultaneously extracting multimodal data recorded in different biological materials (DNA, RNA, protein, etc.). This review summarizes recently developed singlecell multiomics approaches regarding genome, epigenome, and protein profiles with the transcriptome. In particular, we focus on how to anchor or tag molecules from a cell, improve throughputs with sample multiplexing, and record lineages, and we further discuss the future developments of the technology.

ClonoCluster: A method for using clonal origin to inform transcriptome clustering

Clustering cells based on their high-dimensional profiles is an important data reduction process by which researchers infer distinct cellular states. The advent of cellular barcoding, however, provides an alternative means by which to group cells: by their clonal origin. We developed ClonoCluster, a computational method that combines both clone and transcriptome information to create hybrid clusters that weight both kinds of data with a tunable parameter. We generated hybrid clusters across six independent datasets and found that ClonoCluster generated qualitatively different clusters in all cases. The markers of these hybrid clusters were different but had equivalent fidelity to transcriptome-only clusters. The genes most strongly associated with the rearrangements in hybrid clusters were ribosomal function and extracellular matrix genes. We also developed the complementary tool Warp Factor that incorporates clone information in popular 2D visualization techniques like UMAP. Integrating ClonoCluster and Warp Factor revealed biologically relevant markers of cell identity.

Molecular recording: transcriptional data collection into the genome

Advances in regenerative medicine depend upon understanding the complex transcriptional choreography that guides cellular development. Transcriptional molecular recorders, tools that record different transcriptional events into the genome of cells, hold promise to elucidate both the intensity and timing of transcriptional activity at single-cell resolution without requiring destructive multitime point assays. These technologies are dependent on DNA writers, which translate transcriptional signals into stable genomic mutations that encode the duration, intensity, and order of transcriptional events. In this review, we highlight recent progress toward more informative and multiplexable transcriptional recording through the use of three different types of DNA writing - recombineering, Cas1-Cas2 acquisition, and prime editing - and the architecture of the genomic data generated.

TEMPO enables sequential genetic labeling and manipulation of vertebrate cell lineages

During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.

The mouse allantois: new insights at the embryonic-extraembryonic interface

During the early development of Placentalia, a distinctive projection emerges at the posterior embryonic-extraembryonic interface of the conceptus; its fingerlike shape presages maturation into the placental umbilical cord, whose major role is to shuttle fetal blood to and from the chorion for exchange with the mother during pregnancy. Until recently, the biology of the cord's vital vascular anlage, called the body stalk/allantois in humans and simply the allantois in rodents, has been largely unknown. Here, new insights into the development of the mouse allantois are featured, from its origin and mechanism of arterial patterning through its union with the chorion. Key to generating the allantois and its critical functions are the primitive streak and visceral endoderm, which together are sufficient to create the entire fetal-placental connection. Their newly discovered roles at the embryonic-extraembryonic interface challenge conventional wisdom, including the physical limits of the primitive streak, its function as sole purveyor of mesoderm in the mouse, potency of visceral endoderm, and the putative role of the allantois in the germ line. With this working model of allantois development, understanding a plethora of hitherto poorly understood orphan diseases in humans is now within reach. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.

Spatial epitope barcoding reveals clonal tumor patch behaviors

Intratumoral heterogeneity is a seminal feature of human tumors contributing to tumor progression and response to treatment. Current technologies are still largely unsuitable to accurately track phenotypes and clonal evolution within tumors, especially in response to genetic manipulations. Here, we developed epitopes for imaging using combinatorial tagging (EpicTags), which we coupled to multiplexed ion beam imaging (EpicMIBI) for in situ tracking of barcodes within tissue microenvironments. Using EpicMIBI, we dissected the spatial component of cell lineages and phenotypes in xenograft models of small cell lung cancer. We observed emergent properties from mixed clones leading to the preferential expansion of clonal patches for both neuroendocrine and non-neuroendocrine cancer cell states in these models. In a tumor model harboring a fraction of PTEN-deficient cancer cells, we observed a non-autonomous increase of clonal patch size in PTEN wild-type cancer cells. EpicMIBI facilitates in situ interrogation of cell-intrinsic and cell-extrinsic processes involved in intratumoral heterogeneity.

Mastering the use of cellular barcoding to explore cancer heterogeneity

Tumours are often composed of a multitude of malignant clones that are genomically unique, and only a few of them may have the ability to escape cancer therapy and grow as symptomatic lesions. As a result, tumours with a large degree of genomic diversity have a higher chance of leading to patient death. However, clonal fate can be driven by non-genomic features. In this context, new technologies are emerging not only to track the spatiotemporal fate of individual cells and their progeny but also to study their molecular features using various omics analysis. In particular, the recent development of cellular barcoding facilitates the labelling of tens to millions of cancer clones and enables the identification of the complex mechanisms associated with clonal fate in different microenvironments and in response to therapy. In this Review, we highlight the recent discoveries made using lentiviral-based cellular barcoding techniques, namely genetic and optical barcoding. We also emphasize the strengths and limitations of each of these technologies and discuss some of the key concepts that must be taken into consideration when one is designing barcoding experiments. Finally, we suggest new directions to further improve the use of these technologies in cancer research.

Toward predictive engineering of gene circuits

Many synthetic biology applications rely on programming living cells using gene circuits - the assembly and wiring of genetic elements to control cellular behaviors. Extensive progress has been made in constructing gene circuits with diverse functions and applications. For many circuit functions, however, it remains challenging to ensure that the circuits operate in a predictable manner. Although the notion of predictability may appear intuitive, close inspection suggests that it is not always clear what constitutes predictability. We dissect this concept and how it can be confounded by the complexity of a circuit, the complexity of the context, and the interplay between the two. We discuss circuit engineering strategies, in both computation and experiment, that have been used to improve the predictability of gene circuits.

Connecting past and present: single-cell lineage tracing

Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.

Cellular barcoding to decipher clonal dynamics in disease

Cellular barcodes are distinct DNA sequences that enable one to track specific cells across time or space. Recent advances in our ability to detect natural or synthetic cellular barcodes, paired with single-cell readouts of cell state, have markedly increased our knowledge of clonal dynamics and genealogies of the cells that compose a variety of tissues and organs. These advances hold promise to redefine our view of human disease. Here, we provide an overview of cellular barcoding approaches, discuss applications to gain new insights into disease mechanisms, and provide an outlook on future applications. We discuss unanticipated insights gained through barcoding in studies of cancer and blood cell production and describe how barcoding can be applied to a growing array of medical fields, particularly with the increasing recognition of clonal contributions in human diseases.

Spotlighting adult stem cells: advances, pitfalls, and challenges

The existence of stem cells (SCs) at the tip of the cellular differentiation hierarchy has fascinated the scientific community ever since their discovery in the early 1950s to 1960s. Despite the remarkable success of the SC theory and the development of SC-based treatments, fundamental features of SCs remain enigmatic. Recent advances in single-cell lineage tracing, live imaging, and genomic technologies have allowed capture of life histories and transcriptional signatures of individual cells, leaving SCs much less space to 'hide'. Focusing on epithelial SCs and comparing them to other SCs, we discuss new paradigms of the SC niche, dynamics, and pathology, highlighting key open questions in SC biology that need to be resolved for harnessing SC potential in regenerative medicine.

Live-seq enables temporal transcriptomic recording of single cells

Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity1. However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy2,3, thus allowing to couple a cell's ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without inducing major cellular perturbations. As a proof of concept, we show that Live-seq can be used to directly map a cell's trajectory by sequentially profiling the transcriptomes of individual macrophages before and after lipopolysaccharide (LPS) stimulation, and of adipose stromal cells pre- and post-differentiation. In addition, we demonstrate that Live-seq can function as a transcriptomic recorder by preregistering the transcriptomes of individual macrophages that were subsequently monitored by time-lapse imaging after LPS exposure. This enabled the unsupervised, genome-wide ranking of genes on the basis of their ability to affect macrophage LPS response heterogeneity, revealing basal Nfkbia expression level and cell cycle state as important phenotypic determinants, which we experimentally validated. Thus, Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.

A time-resolved, multi-symbol molecular recorder via sequential genome editing

DNA is naturally well suited to serve as a digital medium for in vivo molecular recording. However, contemporary DNA-based memory devices are constrained in terms of the number of distinct 'symbols' that can be concurrently recorded and/or by a failure to capture the order in which events occur1. Here we describe DNA Typewriter, a general system for in vivo molecular recording that overcomes these and other limitations. For DNA Typewriter, the blank recording medium ('DNA Tape') consists of a tandem array of partial CRISPR-Cas9 target sites, with all but the first site truncated at their 5' ends and therefore inactive. Short insertional edits serve as symbols that record the identity of the prime editing guide RNA2 mediating the edit while also shifting the position of the 'type guide' by one unit along the DNA Tape, that is, sequential genome editing. In this proof of concept of DNA Typewriter, we demonstrate recording and decoding of thousands of symbols, complex event histories and short text messages; evaluate the performance of dozens of orthogonal tapes; and construct 'long tape' potentially capable of recording as many as 20 serial events. Finally, we leverage DNA Typewriter in conjunction with single-cell RNA-seq to reconstruct a monophyletic lineage of 3,257 cells and find that the Poisson-like accumulation of sequential edits to multicopy DNA tape can be maintained across at least 20 generations and 25 days of in vitro clonal expansion.

Molecular recorders to track cellular events

DNA tapes could be used to record dynamic molecular and cellular events in animals.

The emerging landscape of spatial profiling technologies

Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.

Scalable biological signal recording in mammalian cells using Cas12a base editors

Biological signal recording enables the study of molecular inputs experienced throughout cellular history. However, current methods are limited in their ability to scale up beyond a single signal in mammalian contexts. Here, we develop an approach using a hyper-efficient dCas12a base editor for multi-signal parallel recording in human cells. We link signals of interest to expression of guide RNAs to catalyze specific nucleotide conversions as a permanent record, enabled by Cas12's guide-processing abilities. We show this approach is plug-and-play with diverse biologically relevant inputs and extend it for more sophisticated applications, including recording of time-delimited events and history of chimeric antigen receptor T cells' antigen exposure. We also demonstrate efficient recording of up to four signals in parallel on an endogenous safe-harbor locus. This work provides a versatile platform for scalable recording of signals of interest for a variety of biological applications.

CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information

A goal of single-cell genome-wide profiling is to reconstruct dynamic transitions during cell differentiation, disease onset and drug response. Single-cell assays have recently been integrated with lineage tracing, a set of methods that identify cells of common ancestry to establish bona fide dynamic relationships between cell states. These integrated methods have revealed unappreciated cell dynamics, but their analysis faces recurrent challenges arising from noisy, dispersed lineage data. In this study, we developed coherent, sparse optimization (CoSpar) as a robust computational approach to infer cell dynamics from single-cell transcriptomics integrated with lineage tracing. Built on assumptions of coherence and sparsity of transition maps, CoSpar is robust to severe downsampling and dispersion of lineage data, which enables simpler experimental designs and requires less calibration. In datasets representing hematopoiesis, reprogramming and directed differentiation, CoSpar identifies early fate biases not previously detected, predicting transcription factors and receptors implicated in fate choice. Documentation and detailed examples for common experimental designs are available at https://cospar.readthedocs.io/ .

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

The role of serum amyloid A1 in the adipogenic differentiation of human adipose-derived stem cells basing on single-cell RNA sequencing analysis

Background

Adipose-derived stem cells (ASCs) are obtained from a variety of sources in vivo where they present in large quantities. These cells are suitable for use in autologous transplantation and the construction of tissue-engineered adipose tissue. Studies have shown that ASCs differentiation is in a high degree of heterogeneity, yet the molecular basis including key regulators of differentiation remains to clarify.

Methods

We performed single-cell RNA sequencing and bioinformatics analysis on both undifferentiated (ASC-GM group) and adipogenically differentiated human ASCs (ASC-AD group, ASCs were cultured in adipogenic inducing medium for 1 week). And then, we verified the results of serum amyloid A1 (SAA1) with western blotting, immunofluorescence staining, oil red O staining. After these experiments, we down-regulated the expression of serum amyloid A1 (SAA1) gene to verify the adipogenic differentiation ability of ASCs.

Results

In single-cell RNA sequence analyzing, we obtained 4415 cells in the ASC-GM group and 4634 cells in the ASC-AD group. The integrated sample cells could be divided into 11 subgroups (0–10 cluster). The cells in cluster 0, 2, 5 were came from ASC-GM group and the cells in cluster 1, 3, 7 came from ASC-AD group. The cells of cluster 4 and 6 came from both ASC-GM and ASC-AD groups. Fatty acid binding protein 4, fatty acid binding protein 5, complement factor D, fatty acid desaturase 1, and insulin like growth factor binding protein 5 were high expressed in category 1 and 7. Regulation of inflammatory response is the rank 1 biological processes. And cellular responses to external stimuli, negative regulation of defense response and acute inflammatory response are included in top 20 biological processes. Based on the MCODE results, we found that SAA1, C-C Motif Chemokine Ligand 5 (CCL5), and Annexin A1 (ANXA1) significantly highly expressed during adipogenic differentiation. Western blot and immunofluorescent staining results showed that SAA1 increased during adipogenesis. And the area of ORO positive staining in siSAA1 cells was significantly lower than in the siControl (negative control) cells.

Conclusions

Our results also indicated that our adipogenic induction was successful, and there was great heterogeneity in the adipogenic differentiation of ASCs. SAA1 with the regulation of inflammatory response were involved in adipogenesis of ASCs based on single-cell RNA sequencing analysis. The data obtained will help to elucidate the intrinsic mechanism of heterogeneity in the differentiation process of stem cells, thus, guiding the regulation of self-renewal and differentiation of adult stem cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-02873-5.

CRISPR in cancer biology and therapy

Over the past decade, CRISPR has become as much a verb as it is an acronym, transforming biomedical research and providing entirely new approaches for dissecting all facets of cell biology. In cancer research, CRISPR and related tools have offered a window into previously intractable problems in our understanding of cancer genetics, the noncoding genome and tumour heterogeneity, and provided new insights into therapeutic vulnerabilities. Here, we review the progress made in the development of CRISPR systems as a tool to study cancer, and the emerging adaptation of these technologies to improve diagnosis and treatment.

A New Generation of Lineage Tracing Dynamically Records Cell Fate Choices

Reconstructing the development of lineage relationships and cell fate mapping has been a fundamental problem in biology. Using advanced molecular biology and single-cell RNA sequencing, we have profiled transcriptomes at the single-cell level and mapped cell fates during development. Recently, CRISPR/Cas9 barcode editing for large-scale lineage tracing has been used to reconstruct the pseudotime trajectory of cells and improve lineage tracing accuracy. This review presents the progress of the latest CbLT (CRISPR-based Lineage Tracing) and discusses the current limitations and potential technical pitfalls in their application and other emerging concepts.

Interactive, integrated analysis of single-cell transcriptomic and phylogenetic data with PhyloVision

Recent advances in CRISPR-Cas9 engineering and single-cell assays have enabled the simultaneous measurement of single-cell transcriptomic and phylogenetic profiles. However, there are few computational tools enabling users to integrate and derive insight from a joint analysis of these two modalities. Here, we describe "PhyloVision": an open-source software for interactively exploring data from both modalities and for identifying and interpreting heritable gene modules whose concerted expression are associated with phylogenetic relationships. PhyloVision provides a feature-rich, interactive, and shareable web-based report for investigating these modules while also supporting several other data and meta-data exploration capabilities. We demonstrate the utility of PhyloVision using a published dataset of metastatic lung adenocarcinoma cells, whose phylogeny was resolved using a CRISPR-Cas9-based lineage-tracing system. Together, we anticipate that PhyloVision and the methods it implements will be a useful resource for scalable and intuitive data exploration for any assay that simultaneously measures cell state and lineage.

Deep distributed computing to reconstruct extremely large lineage trees

Phylogeny estimation (the reconstruction of evolutionary trees) has recently been applied to CRISPR-based cell lineage tracing, allowing the developmental history of an individual tissue or organism to be inferred from a large number of mutated sequences in somatic cells. However, current computational methods are not able to construct phylogenetic trees from extremely large numbers of input sequences. Here, we present a deep distributed computing framework to comprehensively trace accurate large lineages (FRACTAL) that substantially enhances the scalability of current lineage estimation software tools. FRACTAL first reconstructs only an upstream lineage of the input sequences and recursively iterates the same produce for its downstream lineages using independent computing nodes. We demonstrate the utility of FRACTAL by reconstructing lineages from >235 million simulated sequences and from >16 million cells from a simulated experiment with a CRISPR system that accumulates mutations during cell proliferation. We also successfully applied FRACTAL to evolutionary tree reconstructions and to an experiment using error-prone PCR (EP-PCR) for large-scale sequence diversification.

Single cell lineage reconstruction using distance-based algorithms and the R package, DCLEAR

Background

DCLEAR is an R package used for single cell lineage reconstruction. The advances of CRISPR-based gene editing technologies have enabled the prediction of cell lineage trees based on observed edited barcodes from each cell. However, the performance of existing reconstruction methods of cell lineage trees was not accessed until recently. In response to this problem, the Allen Institute hosted the Cell Lineage Reconstruction Dream Challenge in 2020 to crowdsource relevant knowledge from across the world. Our team won sub-challenges 2 and 3 in the challenge competition.

Results

The DCLEAR package contained the R codes, which was submitted in response to sub-challenges 2 and 3. Our method consists of two steps: (1) distance matrix estimation and (2) the tree reconstruction from the distance matrix. We proposed two novel methods for distance matrix estimation as outlined in the DCLEAR package. Using our method, we find that two of the more sophisticated distance methods display a substantially improved level of performance compared to the traditional Hamming distance method. DCLEAR is open source and freely available from R CRAN and from under the GNU General Public License, version 3.

Conclusions

DCLEAR is a powerful resource for single cell lineage reconstruction.

The role of NAD and NAD precursors on longevity and lifespan modulation in the budding yeast, Saccharomyces cerevisiae

Molecular causes of aging and longevity interventions have witnessed an upsurge in the last decade. The resurgent interests in the application of small molecules as potential geroprotectors and/or pharmacogenomics point to nicotinamide adenine dinucleotide (NAD) and its precursors, nicotinamide riboside, nicotinamide mononucleotide, nicotinamide, and nicotinic acid as potentially intriguing molecules. Upon supplementation, these compounds have shown to ameliorate aging related conditions and possibly prevent death in model organisms. Besides being a molecule essential in all living cells, our understanding of the mechanism of NAD metabolism and its regulation remain incomplete owing to its omnipresent nature. Here we discuss recent advances and techniques in the study of chronological lifespan (CLS) and replicative lifespan (RLS) in the model unicellular organism Saccharomyces cerevisiae. We then follow with the mechanism and biology of NAD precursors and their roles in aging and longevity. Finally, we review potential biotechnological applications through engineering of microbial lifespan, and laid perspective on the promising candidature of alternative redox compounds for extending lifespan.

Engineering Brain Organoids: Toward Mature Neural Circuitry with an Intact Cytoarchitecture

The emergence of brain organoids as a model system has been a tremendously exciting development in the field of neuroscience. Brain organoids are a gateway to exploring the intricacies of human-specific neurogenesis that have so far eluded the neuroscience community. Regardless, current culture methods have a long way to go in terms of accuracy and reproducibility. To perfectly mimic the human brain, we need to recapitulate the complex in vivo context of the human fetal brain and achieve mature neural circuitry with an intact cytoarchitecture. In this review, we explore the major challenges facing the current brain organoid systems, potential technical breakthroughs to advance brain organoid techniques up to levels similar to an in vivo human developing brain, and the future prospects of this technology.

Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics

The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.

Ratz et al. present an easy-to-use method to barcode progenitor cells, enabling profiling of cell phenotypes and clonal relations using single-cell and spatial transcriptomics, providing an integrated approach for understanding brain architecture.

Temporal signaling, population control, and information processing through chromatin-mediated gene regulation

Chromatin regulation is a key pathway cells use to regulate gene expression in response to temporal stimuli, and is becoming widely used as a platform for synthetic biology applications. Here, we build a mathematical framework for analyzing the response of genetic circuits containing chromatin regulators to temporal signals in mammalian cell populations. Chromatin regulators can silence genes in an all-or-none fashion at the single-cell level, with individual cells stochastically transitioning between active, reversibly silent, and irreversibly silent gene states at constant rates over time. We integrate this mode of regulation with classical gene regulatory motifs, such as autoregulatory and incoherent feedforward loops, to determine the types of responses achievable with duration-dependent signaling. We demonstrate that repressive regulators without long-term epigenetic memory can filter out high frequency noise, and as part of an autoregulatory loop can precisely tune the fraction of cells in a population that expresses a gene of interest. Additionally, we find that repressive regulators with epigenetic memory can sum up and encode the total duration of their recruitment in the fraction of cells irreversibly silenced and, when included in a feed forward loop, enable perfect adaptation. Last, we use an information theoretic approach to show that all-or-none stochastic silencing can be used by populations to transmit information reliably and with high fidelity even in very simple genetic circuits. Altogether, we show that chromatin-mediated gene control enables a repertoire of complex cell population responses to temporal signals and can transmit higher information levels than previously measured in gene regulation.

Mapping transcriptomic vector fields of single cells

Single-cell (sc)-RNA-seq, together with RNA-velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, that infers absolute RNA velocity, reconstructs continuous vector-field functions that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Synthetic multistability in mammalian cells

In multicellular organisms, gene regulatory circuits generate thousands of molecularly distinct, mitotically heritable states through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and would allow engineering of multicellular programs that require interactions among distinct cell types. We created MultiFate, a naturally inspired, synthetic circuit that supports long-term, controllable, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using a model-based design, we engineered MultiFate circuits that generate as many as seven states, each stable for at least 18 days. MultiFate permits controlled state switching and modulation of state stability through external inputs and can be expanded with additional transcription factors. These results provide a foundation for engineering multicellular behaviors in mammalian cells.

From Cell States to Cell Fates: How Cell Proliferation and Neuronal Differentiation Are Coordinated During Embryonic Development

The central nervous system (CNS) exhibits an extraordinary diversity of neurons, with the right cell types and proportions at the appropriate sites. Thus, to produce brains with specific size and cell composition, the rates of proliferation and differentiation must be tightly coordinated and balanced during development. Early on, proliferation dominates; later on, the growth rate almost ceases as more cells differentiate and exit the cell cycle. Generation of cell diversity and morphogenesis takes place concomitantly. In the vertebrate brain, this results in dramatic changes in the position of progenitor cells and their neuronal derivatives, whereas in the spinal cord morphogenetic changes are not so important because the structure mainly grows by increasing its volume. Morphogenesis is under control of specific genetic programs that coordinately unfold over time; however, little is known about how they operate and impact in the pools of progenitor cells in the CNS. Thus, the spatiotemporal coordination of these processes is fundamental for generating functional neuronal networks. Some key aims in developmental neurobiology are to determine how cell diversity arises from pluripotent progenitor cells, and how the progenitor potential changes upon time. In this review, we will share our view on how the advance of new technologies provides novel data that challenge some of the current hypothesis. We will cover some of the latest studies on cell lineage tracing and clonal analyses addressing the role of distinct progenitor cell division modes in balancing the rate of proliferation and differentiation during brain morphogenesis. We will discuss different hypothesis proposed to explain how progenitor cell diversity is generated and how they challenged prevailing concepts and raised new questions.

A unified framework for measuring selection on cellular lineages and traits

Intracellular states probed by gene expression profiles and metabolic activities are intrinsically noisy, causing phenotypic variations among cellular lineages. Understanding the adaptive and evolutionary roles of such variations requires clarifying their linkage to population growth rates. Extending a cell lineage statistics framework, here we show that a population's growth rate can be expanded by the cumulants of a fitness landscape that characterize how fitness distributes in a population. The expansion enables quantifying the contribution of each cumulant, such as variance and skewness, to population growth. We introduce a function that contains all the essential information of cell lineage statistics, including mean lineage fitness and selection strength. We reveal a relation between fitness heterogeneity and population growth rate response to perturbation. We apply the framework to experimental cell lineage data from bacteria to mammalian cells, revealing distinct levels of growth rate gain from fitness heterogeneity across environments and organisms. Furthermore, third or higher order cumulants' contributions are negligible under constant growth conditions but could be significant in regrowing processes from growth-arrested conditions. We identify cellular populations in which selection leads to an increase of fitness variance among lineages in retrospective statistics compared to chronological statistics. The framework assumes no particular growth models or environmental conditions, and is thus applicable to various biological phenomena for which phenotypic heterogeneity and cellular proliferation are important.

Emerging strategies for the genetic dissection of gene functions, cell types, and neural circuits in the mammalian brain

The mammalian brain is composed of a large number of highly diverse cell types with different molecular, anatomical, and functional features. Distinct cellular identities are generated during development under the regulation of intricate genetic programs and manifested through unique combinations of gene expression. Recent advancements in our understanding of the molecular and cellular mechanisms underlying the assembly, function, and pathology of the brain circuitry depend on the invention and application of genetic strategies that engage intrinsic gene regulatory mechanisms. Here we review the strategies for gene regulation on DNA, RNA, and protein levels and their applications in cell type targeting and neural circuit dissection. We highlight newly emerged strategies and emphasize the importance of combinatorial approaches. We also discuss the potential caveats and pitfalls in current methods and suggest future prospects to improve their comprehensiveness and versatility.

Evolving features of human cortical development and the emerging roles of non-coding RNAs in neural progenitor cell diversity and function

The human cerebral cortex is a uniquely complex structure encompassing an unparalleled diversity of neuronal types and subtypes. These arise during development through a series of evolutionary conserved processes, such as progenitor cell proliferation, migration and differentiation, incorporating human-associated adaptations including a protracted neurogenesis and the emergence of novel highly heterogeneous progenitor populations. Disentangling the unique features of human cortical development involves elucidation of the intricate developmental cell transitions orchestrated by progressive molecular events. Crucially, developmental timing controls the fine balance between cell cycle progression/exit and the neurogenic competence of precursor cells, which undergo morphological transitions coupled to transcriptome-defined temporal states. Recent advances in bulk and single-cell transcriptomic technologies suggest that alongside protein-coding genes, non-coding RNAs exert important regulatory roles in these processes. Interestingly, a considerable number of novel long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have appeared in human and non-human primates suggesting an evolutionary role in shaping cortical development. Here, we present an overview of human cortical development and highlight the marked diversification and complexity of human neuronal progenitors. We further discuss how lncRNAs and miRNAs constitute critical components of the extended epigenetic regulatory network defining intermediate states of progenitors and controlling cell cycle dynamics and fate choices with spatiotemporal precision, during human neurodevelopment.

Mapping single-cell-resolution cell phylogeny reveals cell population dynamics during organ development

Mapping the cell phylogeny of a complex multicellular organism relies on somatic mutations accumulated from zygote to adult. Available cell barcoding methods can record about three mutations per barcode, enabling only low-resolution mapping of the cell phylogeny of complex organisms. Here we developed SMALT, a substitution mutation-aided lineage-tracing system that outperforms the available cell barcoding methods in mapping cell phylogeny. We applied SMALT to Drosophila melanogaster and obtained on average more than 20 mutations on a three-kilobase-pair barcoding sequence in early-adult cells. Using the barcoding mutations, we obtained high-quality cell phylogenetic trees, each comprising several thousand internal nodes with 84-93% median bootstrap support. The obtained cell phylogenies enabled a population genetic analysis that estimates the longitudinal dynamics of the number of actively dividing parental cells (Np) in each organ through development. The Np dynamics revealed the trajectory of cell births and provided insight into the balance of symmetric and asymmetric cell division.

DNA barcode to trace the development and differentiation of cord blood stem cells (Review)

Umbilical cord blood transplantation was first reported in 1980. Since then, additional research has indicated that umbilical cord blood stem cells (UCBSCs) have various advantages, such as multi‑lineage differentiation potential and potent renewal activity, which may be induced to promote their differentiation into a variety of seed cells for tissue engineering and the treatment of clinical and metabolic diseases. Recent studies suggested that UCBSCs are able to differentiate into nerve cells, chondrocytes, hepatocyte‑like cells, fat cells and osteoblasts. The culture of UCBSCs has developed from feeder‑layer to feeder‑free culture systems. The classical techniques of cell labeling and tracing by gene transfection and fluorescent dye and nucleic acid analogs have evolved to DNA barcode technology mediated by transposon/retrovirus, cyclization recombination‑recombinase and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR‑associated protein 9 strategies. DNA barcoding for cell development tracing has advanced to include single cells and single nucleic acid mutations. In the present study, the latest research findings on the development and differentiation, culture techniques and labeling and tracing of UCBSCs are reviewed. The present study may increase the current understanding of UCBSC biology and its clinical applications.

Recent advances in lineage tracing for the kidney

Lineage tracing was originally developed by developmental biologists to identify all progeny of a single cell during morphogenesis. More recently this approach has been applied to other fields, including organ homeostasis and recovery from injury. Modern lineage tracing techniques typically rely on reporter gene expression induced by cell-specific DNA recombination. There have been important scientific advances in the last 10 years that have impacted lineage tracing approaches, including intersectional genetics, optical clearing techniques, and the use of sequencing-based genomic lineage tracing. The latter combines CRISPR-Cas9-based genetic scarring with single-cell RNA-sequencing that, in theory, could allow comprehensive reconstruction of a lineage tree for an entire organism. This review summarizes recent advances in lineage tracing technologies and outlines potential applications for kidney research.