Summary To delineate the ontogeny of the mammalian endoderm, we generated 112,217 single-cell transcriptomes representing all endoderm populations within the mouse embryo until midgestation. By employing graph-based approaches, we modelled differentiating cells for spatio-temporal characterization of developmental trajectories and defined the transcriptional architecture that accompanies the emergence of the first (primitive or extra-embryonic) endodermal population and its sister pluripotent (embryonic) epiblast lineage. We uncovered a relationship between descendants of these two lineages, whereby epiblast cells differentiate into endoderm at two distinct time-points, before and during gastrulation. Trajectories of endoderm cells were mapped as they acquired embryonic versus extra-embryonic fates, and as they spatially converged within the nascent gut endoderm; revealing them to be globally similar but retaining aspects of their lineage history. We observed the regionalized identity of cells along the anterior-posterior axis of the emergent gut tube, reflecting their embryonic or extra-embryonic origin, and their coordinate patterning into organ-specific territories.

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Reconstructing lineage relationships in complex tissues can reveal mechanisms underlying development and disease. Recent methods combine single-cell transcriptomics with mitochondrial DNA variant detection to establish lineage relationships in primary human cells, but are not scalable to interrogate complex tissues. To overcome this limitation, here we develop a technology for highconfidence detection of mitochondrial mutations from high-throughput single-cell RNA-sequencing. We use the new method to identify skewed immune cell expansions in primary human clonal hematopoiesis. Main textSingle-cell RNA-sequencing (scRNA-seq) enables the unbiased assessment of cell states in health and disease 1,2 . Combined acquisition of cell state and genetic information can provide additional insight, such as targeted enrichment of cancer driver mutations from single-cell transcriptomes 3,4 . Separately, combining scRNA-seq with genetic cell barcodes is a powerful method to reveal clonal relationships and evolutionary dynamics of cells within organisms 5,6 . However, this has largely been limited to experimental model systems that can be genetically manipulated to insert cell barcodes. To infer clonal dynamics in primary human cells, recent methods have detected and utilized mitochondrial DNA (mtDNA) mutations as naturally occurring genetic cell barcodes [7][8][9] . The combination of scRNA-seq with mtDNA mutation detection can inform clonal relationships with high confidence, but is currently restricted to expensive, low-throughput, full-length transcript sequencing technologies like SmartSeq2 7,10 . To enable the reconstruction of clonal relationships in complex human tissues, we developed a method that captures genetic variants from high-throughput scRNA-seq platforms: MAESTER, or Mitochondrial Alteration Enrichment from Singlecell Transcriptomes to Establish Relatedness (Figure 1A). MAESTER is compatible with the most common high-throughput scRNA-seq platforms, including 10x Genomics 3' protocols, Seq-Well S 3 , and Drop-seq (Supplemental Figures 1-3) 11,12 . An intermediate step in each of these platforms yields full length cDNA transcripts, from which we enrich all 15 mitochondrial transcripts using pools of primers, while maintaining cell-identifying barcodes (Figure 1B, Supplemental Figure 4). Standard next-generation sequencing with 250 bp reads is then used to obtain the sequence of the amplified mitochondrial transcripts (Figure 1A). We developed a computational toolkit to call mtDNA variants from MAESTER data, the Mitochondrial Alteration Enrichment and Genome Analysis Toolkit (maegatk, Supplemental Figure 5, Methods). Building on previous tools that we developed 8 for mtDNA variant detection from single-cell ATAC or SmartSeq2, maegatk specifically handles technical biases implicit in high-throughput transcriptomic libraries. Critically, maegatk leverages unique molecular identifiers (UMIs) to collapse multiple sequencing reads of the same starting transcript, creating a consensus call for every

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