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A fundamental goal of developmental and stem cell biology is to map the developmental history (ontogeny) of differentiated cell types. Recent advances in high-throughput single-cell sequencing technologies have enabled the construction of comprehensive transcriptional atlases of adult tissues and of developing embryos from measurements of up to millions of individual cells. Parallel advances in sequencing-based lineage-tracing methods now facilitate the mapping of clonal relationships onto these landscapes and enable detailed comparisons between molecular and mitotic histories. Here we review recent progress and challenges, as well as the opportunities that emerge when these two complementary representations of cellular history are synthesized into integrated models of cell differentiation.Understanding developmental trajectories has recently been enabled by progress in modern lineage-tracing methods that combine genetic lineage analysis with omics-based characterization of cell states (particularly transcriptomes). In this Review, Wagner and Klein discuss the conceptual underpinnings, experimental strategies and analytical considerations of these approaches, as well as the biological insights gained.

Our kidneys play a critical role in keeping us healthy, a fact of which we are reminded several times each day. This organ's cellular complexity has hindered progress in understanding the mechanisms underlying chronic kidney disease, which affects 10% of the world's population. Using single-cell transcriptional profiling, Park et al. produced a comprehensive cell atlas of the healthy mouse kidney (see the Perspective by Humphreys). An unexpected cell type in the collecting duct appears to be a transitional state between two known cell types. The transition from one cell type to the other is regulated by the Notch signaling pathway and is associated with metabolic acidosis. The authors also find that genetically distinct kidney diseases with common clinical features share common cellular origins. Science , this issue p. 758 ; see also p. 709 A single-cell atlas of the mouse kidney reveals an unexpected cell type that likely contributes to kidney disease. Our understanding of kidney disease pathogenesis is limited by an incomplete molecular characterization of the cell types responsible for the organ’s multiple homeostatic functions. To help fill this knowledge gap, we characterized 57,979 cells from healthy mouse kidneys by using unbiased single-cell RNA sequencing. On the basis of gene expression patterns, we infer that inherited kidney diseases that arise from distinct genetic mutations but share the same phenotypic manifestation originate from the same differentiated cell type. We also found that the collecting duct in kidneys of adult mice generates a spectrum of cell types through a newly identified transitional cell. Computational cell trajectory analysis and in vivo lineage tracing revealed that intercalated cells and principal cells undergo transitions mediated by the Notch signaling pathway. In mouse and human kidney disease, these transitions were shifted toward a principal cell fate and were associated with metabolic acidosis.

Multicellular systems develop from single cells through distinct lineages. However, current lineage-tracing approaches scale poorly to whole, complex organisms. Here, we use genome editing to progressively introduce and accumulate diverse mutations in a DNA barcode over multiple rounds of cell division. The barcode, an array of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 target sites, marks cells and enables the elucidation of lineage relationships via the patterns of mutations shared between cells. In cell culture and zebrafish, we show that rates and patterns of editing are tunable and that thousands of lineage-informative barcode alleles can be generated. By sampling hundreds of thousands of cells from individual zebrafish, we find that most cells in adult organs derive from relatively few embryonic progenitors. In future analyses, genome editing of synthetic target arrays for lineage tracing (GESTALT) can be used to generate large-scale maps of cell lineage in multicellular systems for normal development and disease.

A single-cell sequencing method is developed that uses transcriptomics and CRISPR–Cas9 technology to investigate clonal relationships in cells present in different zebrafish tissues. Tracing single cells from embryo to adult Determining the adult fate of progenitor cells present during embryonic development is a challenging task because it requires simultaneous knowledge about the lineage and identity of the cells at a single-cell level. Alexander van Oudenaarden and colleagues have developed a new method to tackle this challenge. ScarTrace relies on single-cell transcriptome sequencing and barcodes ('scars') introduced by CRISPR–Cas9 in individual progenitor cells. The authors use ScarTrace to investigate lineage relationships in cells present in different zebrafish tissues. In the future, such a method could make it possible to match all embryonic cell types to all adult cell types, and to reconstruct how the body emerges from a single cell. Embryonic development is a crucial period in the life of a multicellular organism, during which limited sets of embryonic progenitors produce all cells in the adult body. Determining which fate these progenitors acquire in adult tissues requires the simultaneous measurement of clonal history and cell identity at single-cell resolution, which has been a major challenge. Clonal history has traditionally been investigated by microscopically tracking cells during development 1 , 2 , monitoring the heritable expression of genetically encoded fluorescent proteins 3 and, more recently, using next-generation sequencing technologies that exploit somatic mutations 4 , microsatellite instability 5 , transposon tagging 6 , viral barcoding 7 , CRISPR–Cas9 genome editing 8 , 9 , 10 , 11 , 12 , 13 and Cre– loxP recombination 14 . Single-cell transcriptomics 15 provides a powerful platform for unbiased cell-type classification. Here we present ScarTrace, a single-cell sequencing strategy that enables the simultaneous quantification of clonal history and cell type for thousands of cells obtained from different organs of the adult zebrafish. Using ScarTrace, we show that a small set of multipotent embryonic progenitors generate all haematopoietic cells in the kidney marrow, and that many progenitors produce specific cell types in the eyes and brain. In addition, we study when embryonic progenitors commit to the left or right eye. ScarTrace reveals that epidermal and mesenchymal cells in the caudal fin arise from the same progenitors, and that osteoblast-restricted precursors can produce mesenchymal cells during regeneration. Furthermore, we identify resident immune cells in the fin with a distinct clonal origin from other blood cell types. We envision that similar approaches will have major applications in other experimental systems, in which the matching of embryonic clonal origin to adult cell type will ultimately allow reconstruction of how the adult body is built from a single cell.

Just how related are reptilian and mammalian brains? Tosches et al. used single-cell transcriptomics to study turtle, lizard, mouse, and human brain samples. They assessed how the mammalian six-layered cortex might be derived from the reptilian three-layered cortex. Despite a lack of correspondence between layers, mammalian astrocytes and adult neural stem cells shared evolutionary origins. General classes of interneuron types were represented across the evolutionary span, although subtypes were species-specific. Pieces of the much-folded mammalian hippocampus were represented as adjacent fields in the reptile brains. Science , this issue p. 881 Transcriptomics tracks the mix of evolutionary derivation and species-specific elaboration that generates brains from reptiles to mammals. Computations in the mammalian cortex are carried out by glutamatergic and γ-aminobutyric acid–releasing (GABAergic) neurons forming specialized circuits and areas. Here we asked how these neurons and areas evolved in amniotes. We built a gene expression atlas of the pallium of two reptilian species using large-scale single-cell messenger RNA sequencing. The transcriptomic signature of glutamatergic neurons in reptilian cortex suggests that mammalian neocortical layers are made of new cell types generated by diversification of ancestral gene-regulatory programs. By contrast, the diversity of reptilian cortical GABAergic neurons indicates that the interneuron classes known in mammals already existed in the common ancestor of all amniotes.

The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. Furthermore, this representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types. For the prediction of the proposed neighbor distances, an adapted U-Net convolutional neural network (CNN) with two decoder paths is used. In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking. The adapted tracking algorithm includes a movement estimation in the cost function to re-link tracks with missing segmentation masks over a short sequence of frames. Our combined tracking by detection method has proven its potential in the IEEE ISBI 2020 Cell Tracking Challenge ( http://celltrackingchallenge.net/ ) where we achieved as team KIT-Sch-GE multiple top three rankings including two top performances using a single segmentation model for the diverse data sets.

An artificial recombination locus, Polylox , that can generate hundreds of thousands of individual barcodes is used to trace the fates of haematopoietic stem cells in mice. Barcode tracking blood stem cells Transplantation-based assays of haematopoietic stem cells (HSCs) and progenitors isolated on the basis of the expression of their surface markers have inferred that the haematopoietic lineage follows a tree-like structure that starts from a long-term multipotent HSC at its base and splits into a few major branches. However, recent data question the existence of this structure, instead supporting the idea that the blood lineage is sustained by several fate-restricted progenitors. Hans-Reimer Rodewald and colleagues have developed a DNA recombination locus based on the Cre– loxP system that can tag single cells using several hundred thousand barcodes. They introduce the labelling in mouse embryos and track HSCs during their life. Surprisingly, the adult HSC compartment is a mosaic of HSC clones derived from embryos and contributes with different proportion to blood lineage, some multilineage and others of restricted fates, according to a pattern that is consistent within clones. However, they define an early split of fate between myeloid erythroid and lymphocyte development which agrees with the tree-like structure. Developmental deconvolution of complex organs and tissues at the level of individual cells remains challenging. Non-invasive genetic fate mapping 1 has been widely used, but the low number of distinct fluorescent marker proteins limits its resolution. Much higher numbers of cell markers have been generated using viral integration sites 2 , viral barcodes 3 , and strategies based on transposons 4 and CRISPR–Cas9 genome editing 5 ; however, temporal and tissue-specific induction of barcodes in situ has not been achieved. Here we report the development of an artificial DNA recombination locus (termed Polylox ) that enables broadly applicable endogenous barcoding based on the Cre– loxP recombination system 6 , 7 . Polylox recombination in situ reaches a practical diversity of several hundred thousand barcodes, allowing tagging of single cells. We have used this experimental system, combined with fate mapping, to assess haematopoietic stem cell (HSC) fates in vivo . Classical models of haematopoietic lineage specification assume a tree with few major branches. More recently, driven in part by the development of more efficient single-cell assays and improved transplantation efficiencies, different models have been proposed, in which unilineage priming may occur in mice and humans at the level of HSCs 8 , 9 , 10 . We have introduced barcodes into HSC progenitors in embryonic mice, and found that the adult HSC compartment is a mosaic of embryo-derived HSC clones, some of which are unexpectedly large. Most HSC clones gave rise to multilineage or oligolineage fates, arguing against unilineage priming, and suggesting coherent usage of the potential of cells in a clone. The spreading of barcodes, both after induction in embryos and in adult mice, revealed a basic split between common myeloid–erythroid development and common lymphocyte development, supporting the long-held but contested view of a tree-like haematopoietic structure.

Direct lineage reprogramming involves the conversion of cellular identity. Single-cell technologies are useful for deconstructing the considerable heterogeneity that emerges during lineage conversion. However, lineage relationships are typically lost during cell processing, complicating trajectory reconstruction. Here we present ‘CellTagging’, a combinatorial cell-indexing methodology that enables parallel capture of clonal history and cell identity, in which sequential rounds of cell labelling enable the construction of multi-level lineage trees. CellTagging and longitudinal tracking of fibroblast to induced endoderm progenitor reprogramming reveals two distinct trajectories: one leading to successfully reprogrammed cells, and one leading to a ‘dead-end’ state, paths determined in the earliest stages of lineage conversion. We find that expression of a putative methyltransferase, Mettl7a1 , is associated with the successful reprogramming trajectory; adding Mettl7a1 to the reprogramming cocktail increases the yield of induced endoderm progenitors. Together, these results demonstrate the utility of our lineage-tracing method for revealing the dynamics of direct reprogramming. Combinatorial tagging of single cells using expressed DNA barcodes, delivered by a lentiviral vector, is used to track individual cells and reconstruct their lineages and trajectories during cell fate reprogramming.

This Review covers the steps required to create high-quality image-based profiles from high-throughput microscopy images. Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

We introduce the Microenvironment Cell Populations-counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. We present in vitro mRNA mixture and ex vivo immunohistochemical data that quantitatively support the validity of our method’s estimates. Additionally, we demonstrate that MCP-counter overcomes several limitations or weaknesses of previously proposed computational approaches. MCP-counter is applied to draw a global picture of immune infiltrates across human healthy tissues and non-hematopoietic human tumors and recapitulates microenvironment-based patient stratifications associated with overall survival in lung adenocarcinoma and colorectal and breast cancer.