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Intrachromosomal amplification of chromosome 21 defines a subtype of high-risk childhood acute lymphoblastic leukemia (iAMP21-ALL) characterized by copy number changes and complex rearrangements of chromosome 21. The genomic basis of iAMP21-ALL and the pathogenic role of the region of amplification of chromosome 21 to leukemogenesis remain incompletely understood. Here, using integrated whole genome and transcriptome sequencing of 124 iAMP21-ALL patients, including rare cases arising in the context of constitutional chromosomal aberrations, we identified subgroups of iAMP21-ALL according to patterns of copy number alteration and structural variation. This large dataset enabled formal delineation of a 7.8 Mb common region of amplification harboring 71 genes, 43 of which are differentially expressed compared to non-iAMP21-ALL cases, and including multiple genes implicated in the pathogenesis of acute leukemia: CHAF1B, DYRK1A, ERG, HMGN1 and RUNX1. Using multimodal single cell genomic profiling, including single cell whole genome sequencing of two cases, we documented clonal heterogeneity and genomic evolution, formally demonstrating that acquisition of the iAMP21-chromosome is an early event that may undergo progressive amplification during disease ontogeny. We show that UV mutational signatures and high mutation load are characteristic secondary genetic features. Although the genomic alterations of chromosome 21 are variable, these integrated genomic analyses and demonstration of an extended common minimal region of amplification broaden the definition of iAMP21-ALL for more precise diagnosis using cytogenetic or genomic methods to inform clinical management.

Strong nuclear staining of mutant TP53-R337H protein in normal tissue of carriers who developed ACC.

Mouse Cohorts and Associated Pathology

The TP53-R337H founder mutation exists at a high frequency throughout southern Brazil and represents one of the most common germline TP53 mutations reported to date. It was identified in pediatric adrenocortical tumors in families with a low incidence of cancer. The R337H mutation has since been found in association with early-onset breast cancers and Li–Fraumeni syndrome (LFS). To study this variability in tumor susceptibility, we generated a knockin mutant p53 mouse model (R334H). Endogenous murine p53-R334H protein was naturally expressed at high levels in multiple tissues and was functionally compromised in a tissue- and stress-specific manner. Mutant p53-R334H mice developed tumors with long latency and incomplete penetrance, consistent with many human carriers being at a low but elevated risk for cancer. These findings suggest the involvement of additional cooperating genetic alterations when TP53-R337H occurs in the context of LFS, which has important implications for genetic counseling and long-term clinical follow-up.Significance:A p53-R334H knockin mouse serves as an important model for studying the most common inherited germline TP53 mutation (R337H) that is associated with variable tumor susceptibility.

Supplemental Materials and Methods and Supplementary Figure Legends

The TP53-R337H founder mutation exists at a high frequency throughout southern Brazil and represents one of the most common germline TP53 mutations reported to date. It was identified in pediatric adrenocortical tumors in families with a low incidence of cancer. The R337H mutation has since been found in association with early-onset breast cancers and Li–Fraumeni syndrome (LFS). To study this variability in tumor susceptibility, we generated a knockin mutant p53 mouse model (R334H). Endogenous murine p53-R334H protein was naturally expressed at high levels in multiple tissues and was functionally compromised in a tissue- and stress-specific manner. Mutant p53-R334H mice developed tumors with long latency and incomplete penetrance, consistent with many human carriers being at a low but elevated risk for cancer. These findings suggest the involvement of additional cooperating genetic alterations when TP53-R337H occurs in the context of LFS, which has important implications for genetic counseling and long-term clinical follow-up. Significance: A p53-R334H knockin mouse serves as an important model for studying the most common inherited germline TP53 mutation (R337H) that is associated with variable tumor susceptibility.

A lack of relevant genetic models and cell lines hampers our understanding of hepatoblastoma pathogenesis and the development of new therapies for this neoplasm. We report a liver-specific MYC-driven hepatoblastoma murine model that faithfully recapitulates the pathological features of mixed fetal and embryonal hepatoblastoma, with transcriptomics resembling the high-risk gene signatures of the human disease. Single-cell RNA-sequencing and spatial transcriptomics identified distinct subpopulations of hepatoblastoma cells. After deriving cell lines from the mouse model, we mapped the cancer dependency genes using CRISPR-Cas9 screening and identified druggable targets shared with human hepatoblastoma (i.e., CDK7, CDK9, PRMT1, PRMT5). Our screen also revealed oncogenes and tumor suppressor genes in hepatoblastoma that engage multiple, druggable cancer signaling pathways. Chemotherapy is critical for human hepatoblastoma treatment. A genetic mapping of doxorubicin response by CRISPR-Cas9 screening identified modifiers whose loss-of-function synergizes with (e.g., PRKDC) or antagonizes (e.g., apoptosis genes) with the effect of chemotherapy. The combination of PRKDC inhibition and doxorubicin-based chemotherapy greatly enhances therapeutic efficacy. These studies have provided a useful set of resources including disease models suitable to identify and validate potential therapeutic targets in human high-risk hepatoblastoma.

Sequencing cases without matched healthy controls hinders prioritization of germline disease-predisposition genes. To circumvent this problem, genotype summary counts from public data sets can serve as controls. However, systematic inflation and false positives can arise if confounding factors are not addressed. We propose a new framework, consistent summary counts based rare variant burden test (CoCoRV), to address these challenges. CoCoRV has consistent variant quality control and filtering, ethnicity-stratified rare variant association test, accurate estimation of inflation factors, powerful FDR control, and can detect rare variants in high linkage disequilibrium. When we applied CoCoRV to pediatric cancer cohorts, the top genes identified were cancer-predisposition genes. We also applied CoCoRV to identify disease-predisposition genes in adult brain tumors and amyotrophic lateral sclerosis. Given that potential confounding factors were well controlled after applying the framework, CoCoRV provides a cost-effective solution to prioritizing disease-risk genes enriched with rare pathogenic variants.

Sequencing cases without matched healthy controls hinders prioritization of germline disease-predisposition genes. To circumvent this problem, genotype summary counts from public data sets can serve as controls. However, systematic inflation and false positives can arise if confounding factors are not controlled. We propose a framework, consistent summary counts based rare variant burden test (CoCoRV), to address these challenges. CoCoRV implements consistent variant quality control and filtering, ethnicity-stratified rare variant association test, accurate estimation of inflation factors, powerful FDR control, and detection of rare variant pairs in high linkage disequilibrium. When we applied CoCoRV to pediatric cancer cohorts, the top genes identified were cancer-predisposition genes. We also applied CoCoRV to identify disease-predisposition genes in adult brain tumors and amyotrophic lateral sclerosis. Given that potential confounding factors were well controlled after applying the framework, CoCoRV provides a cost-effective solution to prioritizing disease-risk genes enriched with rare pathogenic variants.

Individuals with monogenic disorders can experience variable phenotypes that are influenced by genetic variation. To investigate this in sickle cell disease (SCD), we performed whole-genome sequencing (WGS) of 722 individuals with hemoglobin HbSS or HbSβ0-thalassemia from Baylor College of Medicine and from the St. Jude Children’s Research Hospital Sickle Cell Clinical Research and Intervention Program (SCCRIP) longitudinal cohort study. We developed pipelines to identify genetic variants that modulate sickle hemoglobin polymerization in red blood cells and combined these with pain-associated variants to build a polygenic score (PGS) for acute vaso-occlusive pain (VOP). Overall, we interrogated the α-thalassemia deletion −α3.7 and 133 candidate single-nucleotide polymorphisms (SNPs) across 66 genes for associations with VOP in 327 SCCRIP participants followed longitudinally over 6 years. Twenty-one SNPs in 9 loci were associated with VOP, including 3 (BCL11A, MYB, and the β-like globin gene cluster) that regulate erythrocyte fetal hemoglobin (HbF) levels and 6 (COMT, TBC1D1, KCNJ6, FAAH, NR3C1, and IL1A) that were associated previously with various pain syndromes. An unweighted PGS integrating all 21 SNPs was associated with the VOP event rate (estimate, 0.35; standard error, 0.04; P = 5.9 × 10−14) and VOP event occurrence (estimate, 0.42; standard error, 0.06; P = 4.1 × 10−13). These associations were stronger than those of any single locus. Our findings provide insights into the genetic modulation of VOP in children with SCD. More generally, we demonstrate the utility of WGS for investigating genetic contributions to the variable expression of SCD-associated morbidities.

Amyotrophic lateral sclerosis (ALS) is a multi‐system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine‐learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post‐mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS., Single nucleotide polymorphisms (SNPs) previously identified through genome‐wide association studies as risk factors for amyotrophic lateral sclerosis (ALS) and/or frontotemporal dementia (FTD) further associate in a polygenic manner with risk for cognitive dysfunction in ALS and related disorders.

Shufeng Jiedu capsule (SFJDC) is a traditional Chinese medicine, which has been used for the treatment of respiratory infections for more than thirty years in Hunan (China). SFJDC protected rats against LPS-induced acute lung injury (ALI); however, the molecular mechanisms underlying the therapeutic effects of SFJDC remain unclear. Therefore, this study aimed at analyzing the major anti-inflammatory compounds of SFJDC and exploring the underlying molecular mechanisms. SFJDC dissolved in water was fingerprinted by UPLC/Q-TOF. Inflammation response was assessed by histopathological examination and ELISA assay. Arterial blood gases were also analyzed to evaluate the function of rat lungs. The expression levels of Kelch-like ECH-associating protein 1 (Keap1), Nrf2, heme oxygenase-1 (HO1), Cullin 3 (CUL3) and NQO1 were analyzed by Western blotting. Results indicated that SFJDC alleviated inflammation response by reducing the level of inflammatory cytokines, and upregulation of glutathione-S-transferase (GST) and superoxide dismutase (SOD) in lung tissues. Furthermore, SFJDC suppressed LPS-induced upregulation of Keap 1 and CUL3 in rat lungs. The expression of NRF2 HO1 and NQO1 were further upregulated by SFJDC in the presence of LPS, indicating that SFJDC might activate the NRF2-associated antioxidant pathway. In conclusion, SFJDC treatment may protect the rat lungs from LPS by alleviating the inflammation response via NRF2-associated antioxidant pathway.

Significance Primary template-directed amplification (PTA) is a major improvement in whole genome amplification, which is required to study intratissue cellular evolution. As presented in the manuscript, PTA produces significantly improved and reproducible genome sequencing coverage and variant detection from a single genome of a single cell. Applications of measuring genetic diversity from single cells with PTA presented in the manuscript include examining the acquisition of genetic changes during normal development and aging, measuring the consequences of specific perturbations such as genome editing, and characterizing the evolution of clonal populations during cancer formation., Improvements in whole genome amplification (WGA) would enable new types of basic and applied biomedical research, including studies of intratissue genetic diversity that require more accurate single-cell genotyping. Here, we present primary template-directed amplification (PTA), an isothermal WGA method that reproducibly captures >95% of the genomes of single cells in a more uniform and accurate manner than existing approaches, resulting in significantly improved variant calling sensitivity and precision. To illustrate the types of studies that are enabled by PTA, we developed direct measurement of environmental mutagenicity (DMEM), a tool for mapping genome-wide interactions of mutagens with single living human cells at base-pair resolution. In addition, we utilized PTA for genome-wide off-target indel and structural variant detection in cells that had undergone CRISPR-mediated genome editing, establishing the feasibility for performing single-cell evaluations of biopsies from edited tissues. The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate WGA, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.

The TP53-R337H founder mutation exists at a high frequency throughout southern Brazil and represents one of the most common germline TP53 mutations reported to date. It was identified in pediatric adrenocortical tumors in families with a low incidence of cancer. The R337H mutation has since been found in association with early-onset breast cancers and Li-Fraumeni syndrome (LFS). To study this variability in tumor susceptibility, we generated a knockin mutant p53 mouse model (R334H). Endogenous murine p53-R334H protein was naturally expressed at high levels in multiple tissues and was functionally compromised in a tissue- and stress-specific manner. Mutant p53-R334H mice developed tumors with long latency and incomplete penetrance, consistent with many human carriers being at a low but elevated risk for cancer. These findings suggest the involvement of additional cooperating genetic alterations when TP53-R337H occurs in the context of LFS, which has important implications for genetic counseling and long-term clinical follow-up. SIGNIFICANCE: A p53-R334H knockin mouse serves as an important model for studying the most common inherited germline TP53 mutation (R337H) that is associated with variable tumor susceptibility.

The XAF1-E134* variant increases the cancer risk for carriers of the TP53-R337H allele., Cancer risk is highly variable in carriers of the common TP53-R337H founder allele, possibly due to the influence of modifier genes. Whole-genome sequencing identified a variant in the tumor suppressor XAF1 (E134*/Glu134Ter/rs146752602) in a subset of R337H carriers. Haplotype-defining variants were verified in 203 patients with cancer, 582 relatives, and 42,438 newborns. The compound mutant haplotype was enriched in patients with cancer, conferring risk for sarcoma (P = 0.003) and subsequent malignancies (P = 0.006). Functional analyses demonstrated that wild-type XAF1 enhances transactivation of wild-type and hypomorphic TP53 variants, whereas XAF1-E134* is markedly attenuated in this activity. We propose that cosegregation of XAF1-E134* and TP53-R337H mutations leads to a more aggressive cancer phenotype than TP53-R337H alone, with implications for genetic counseling and clinical management of hypomorphic TP53 mutant carriers.

Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients, however factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N=327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort, and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N=90) as well as post mortem motor cortical neuronal loss (N=87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.

Background Although multiple genes have been implicated in bipolar disorder (BD), they explain only a small proportion of its heritability. Identifying additional BD risk variants may be impaired by phenotypic heterogeneity, which is usually not taken into account in genome-wide association studies (GWAS). BD with early age at onset is a more homogeneous familial form of the disorder associated with greater symptom severity. Methods We conducted a GWAS of early-onset BD (onset of mania/hypomania ≤19 years old) in a discovery sample of 419 cases and 1034 controls and a replication sample of 181 cases and 777 controls. These two samples were meta-analyzed, followed by replication of one signal in a third independent sample of 141 cases and 746 controls. Results No single nucleotide polymorphism (SNP) associations were genome-wide significant in the discovery sample. Of the top 15 SNPs in the discovery analysis, rs114034759 in the muskelin (MKLN1) gene was nominally significant in the replication analysis, and was among the top associations in the meta-analysis (p=2.63E−06, OR=1.9). In the third sample, this SNP was again associated with early-onset BD (p=0.036, OR=1.6). Gene expression analysis showed that the rs114034759 risk allele is associated with decreased hippocampal MKLN1 expression. Limitations The sample sizes of the early-onset BD subgroups were relatively small. Conclusions Our results suggest MKLN1 is associated with early-onset BD. MKLN1 regulates cellular trafficking of GABA-A receptors, which is involved in synaptic transmission and plasticity, and is implicated in the mechanism of action of a group of antiepileptic mood stabilizers. These results therefore indicate that GABAergic neurotransmission may be implicated in early-onset BD. We propose that an increase in GABA-A receptors in the hippocampus in BD patients due to lower MKLN1 expression might increase the excitability during the GABA-excited early phase of young neurons, leading to an increased risk of developing a manic/hypomanic episode. Further studies are needed to test this model.

Graphical abstract, Accounting for batch effects, especially latent batch effects, in differential expression (DE) analysis is critical for identifying true biological effects. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for quantifying cell-to-cell variation in transcript abundance and characterizing cellular dynamics. Although many scRNA-seq DE analysis methods accommodate known batch variables, their performance has not been systematically evaluated. Moreover, the challenge of accounting for latent batch variables in scRNA-seq DE analysis is largely unmet. In contrast, many methods have been developed to account for batch variables (either known or latent) in other high-dimensional data, especially bulk RNA-seq. We extensively evaluate 11 methods for batch variables in different scRNA-seq DE analysis scenarios, with a primary focus on latent batch variables. We demonstrate that for known batch variables, incorporating them as covariates into a regression model outperformed approaches using a batch-corrected matrix. For latent batches, fixed effects models have inflated FDRs, whereas aggregation-based methods and mixed effects models have significant power loss. Surrogate variable based methods generally control the FDR well while achieving good power with small group effects. However, their performance (except that of SVA) deteriorated substantially in scenarios involving large group effects and/or group label impurity. In these settings, SVA achieves relatively good performance despite an occasionally inflated FDR (up to 0.2). Finally we make the following recommendations for scRNA-seq DE analysis: 1) incorporate known batch variables instead of using batch-corrected data; and 2) employ SVA for latent batch correction. However, better methods are still needed to fully unleash the power of scRNA-seq.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing the cell-to-cell variation and cellular dynamics in populations which appear homogeneous otherwise in basic and translational biological research. However, significant challenges arise in the analysis of scRNA-seq data, including the low signal-to-noise ratio with high data sparsity, potential batch effects, scalability problems when hundreds of thousands of cells are to be analyzed among others. The inherent complexities of scRNA-seq data and dynamic nature of cellular processes lead to suboptimal performance of many currently available algorithms, even for basic tasks such as identifying biologically meaningful heterogeneous subpopulations. In this study, we developed the Latent Cellular Analysis (LCA), a machine learning–based analytical pipeline that combines cosine-similarity measurement by latent cellular states with a graph-based clustering algorithm. LCA provides heuristic solutions for population number inference, dimension reduction, feature selection, and control of technical variations without explicit gene filtering. We show that LCA is robust, accurate, and powerful by comparison with multiple state-of-the-art computational methods when applied to large-scale real and simulated scRNA-seq data. Importantly, the ability of LCA to learn from representative subsets of the data provides scalability, thereby addressing a significant challenge posed by growing sample sizes in scRNA-seq data analysis.

Patients harboring germline pathogenic biallelic variants in genes involved in the recognition and repair of DNA damage are known to have a substantially increased cancer risk. Emerging evidence suggests that individuals harboring heterozygous variants in these same genes may also be at heightened, albeit lesser, risk for cancer. Herein, we sought to determine whether heterozygous variants in RECQL4, the gene encoding an essential DNA helicase that is defective in children with the autosomal recessive cancer-predisposing condition Rothmund–Thomson syndrome (RTS), are associated with increased risk for childhood cancer. To address this question, we interrogated germline sequence data from 4435 pediatric cancer patients at St. Jude Children's Research Hospital and 1127 from the National Cancer Institute Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database and identified 24 (0.43%) who harbored loss-of-function (LOF) RECQL4 variants, including five of 249 (2.0%) with osteosarcoma (OS). These RECQL4 variants were significantly overrepresented in children with OS, the cancer most frequently observed in patients with RTS, as compared to 134,187 noncancer controls in the Genome Aggregation Database (gnomAD v2.1; P = 0.00087, odds ratio [OR] = 7.1, 95% CI, 2.9–17). Nine of the 24 (38%) individuals possessed the same c.1573delT (p.Cys525Alafs) variant located in the highly conserved DNA helicase domain, suggesting that disruption of this domain is central to oncogenesis. Altogether these data expand our understanding of the genetic factors predisposing to childhood cancer and reveal a novel association between heterozygous RECQL4 LOF variants and development of pediatric OS.

Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.

Kernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem relies on computationally intensive resampling methods. Here, we derive an exact test for KAT with continuous traits, which resolve the small sample conservatism of KAT without the need for resampling. The exact test has significantly improved power to detect association for microbiome studies. For binary traits, we propose a similar approximate test, and we show that the approximate test is very powerful for a wide range of kernels including common variant- and microbiome-based kernels, and the approximate test controls the type I error well for these kernels. In contrast, the sequence kernel association tests have slightly inflated genomic inflation factors after small sample adjustment. Extensive simulations and application to a real microbiome association study are used to demonstrate the utility of our method.

There are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 760). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10–5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.

The interaction between extrinsic factors and intrinsic signal strength governs thymocyte development, but the mechanisms linking them remain elusive. We report that mechanistic target of rapamycin complex 1 (mTORC1) couples microenvironmental cues with metabolic programs to orchestrate the reciprocal development of two fundamentally distinct T cell lineages, the αβ and γδ T cells. Developing thymocytes dynamically engage metabolic programs including glycolysis and oxidative phosphorylation, as well as mTORC1 signaling. Loss of RAPTOR-mediated mTORC1 activity impairs the development of αβ T cells but promotes γδ T cell generation, associated with disrupted metabolic remodeling of oxidative and glycolytic metabolism. Mechanistically, we identify mTORC1-dependent control of reactive oxygen species production as a key metabolic signal in mediating αβ and γδ T cell development, and perturbation of redox homeostasis impinges upon thymocyte fate decisions and mTORC1-associated phenotypes. Furthermore, single-cell RNA sequencing and genetic dissection reveal that mTORC1 links developmental signals from T cell receptors and NOTCH to coordinate metabolic activity and signal strength. Our results establish mTORC1-driven metabolic signaling as a decisive factor for reciprocal αβ and γδ T cell development and provide insight into metabolic control of cell signaling and fate decisions.

Read counting and unique molecular identifier (UMI) counting are the principal gene expression quantification schemes used in single-cell RNA-sequencing (scRNA-seq) analysis. By using multiple scRNA-seq datasets, we reveal distinct distribution differences between these schemes and conclude that the negative binomial model is a good approximation for UMI counts, even in heterogeneous populations. We further propose a novel differential expression analysis algorithm based on a negative binomial model with independent dispersions in each group (NBID). Our results show that this properly controls the FDR and achieves better power for UMI counts when compared to other recently developed packages for scRNA-seq analysis. Electronic supplementary material The online version of this article (10.1186/s13059-018-1438-9) contains supplementary material, which is available to authorized users.