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Iron and heme play central roles in the production of red blood cells, but the underlying mechanisms remain incompletely understood. Heme-regulated eIF2α kinase (HRI) controls translation by phosphorylating eIF2α. Here, we investigate the global impact of iron, heme, and HRI on protein translation in vivo in murine primary erythroblasts using ribosome profiling. We validate the known role of HRI-mediated translational stimulation of integratedstressresponse mRNAs during iron deficiency in vivo. Moreover, we find that the translation of mRNAs encoding cytosolic and mitochondrial ribosomal proteins is substantially repressed by HRI during iron deficiency, causing a decrease in cytosolic and mitochondrial protein synthesis. The absence of HRI during iron deficiency elicits a prominent cytoplasmic unfolded protein response and impairs mitochondrial respiration. Importantly, ATF4 target genes are activated during iron deficiency to maintain mitochondrial function and to enable erythroid differentiation. We further identify GRB10 as a previously unappreciated regulator of terminal erythropoiesis.

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

Genome-wide association studies in combination with single-cell genomic atlases can provide insights into the mechanisms of disease-causal genetic variation. However, identification of disease-relevant or trait-relevant cell types, states and trajectories is often hampered by sparsity and noise, particularly in the analysis of single-cell epigenomic data. To overcome these challenges, we present SCAVENGE, a computational algorithm that uses network propagation to map causal variants to their relevant cellular context at single-cell resolution. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation, applying the method to blood traits at distinct stages of human hematopoiesis, to monocyte subsets that increase the risk for severe Coronavirus Disease 2019 (COVID-19) and to intermediate lymphocyte developmental states that predispose to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data.

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Hematopoiesis, or the process of blood cell production, is a paradigm of multi‐lineage cellular differentiation that has been extensively studied, yet in many aspects remains incompletely understood. Nearly all clinically measured hematopoietic traits exhibit extensive variation and are highly heritable, underscoring the importance of genetic variation in these processes. This review explores how human genetics have illuminated our understanding of hematopoiesis in health and disease. The study of rare mutations in blood and immune disorders has elucidated novel roles for regulators of hematopoiesis and uncovered numerous important molecular pathways, as seen through examples such as Diamond‐Blackfan anemia and the GATA2 deficiency syndromes. Additionally, population studies of common genetic variation have revealed mechanisms by which human hematopoiesis can be modulated. We discuss advances in functionally characterizing common variants associated with blood cell traits and discuss therapeutic insights, such as the discovery of BCL11A as a modulator of fetal hemoglobin expression. Finally, as genetic techniques continue to evolve, we discuss the prospects, challenges, and unanswered questions that lie ahead in this burgeoning field.

The molecular regulation of human hematopoietic stem cell (HSC) maintenance is therapeutically important, but limitations in experimental systems and interspecies variation have constrained our knowledge of this process. Here, we have studied a rare genetic disorder due to MECOM haploinsufficiency, characterized by an early-onset absence of HSCs in vivo. By generating a faithful model of this disorder in primary human HSCs and coupling functional studies with integrative single-cell genomic analyses, we uncover a key transcriptional network involving hundreds of genes that is required for HSC maintenance. Through our analyses, we nominate cooperating transcriptional regulators and identify how MECOM prevents the CTCF-dependent genome reorganization that occurs as HSCs differentiate. We show that this transcriptional network is co-opted in high-risk leukemias, thereby enabling these cancers to acquire stem cell properties. Collectively, we illuminate a regulatory network necessary for HSC self-renewal through the study of a rare experiment of nature.

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