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RNA-Seq of single cells has been limited by biases in transcript coverage and unknown technical variability. Ramsköld et al . describe a protocol to reproducibly recover full-length transcripts and use it to quantitatively analyze splice isoforms in single cells. Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease- and cell type–specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.

X-chromosome inactivation and X-upregulation are the fundamental modes of chromosome-wide gene regulation that collectively achieve dosage compensation in mammals, but the regulatory link between the two remains elusive and the X-upregulation dynamics are unknown. Here, we use allele-resolved single-cell RNA-seq combined with chromatin accessibility profiling and finely dissect their separate effects on RNA levels during mouse development. Surprisingly, we uncover that X-upregulation elastically tunes expression dosage in a sex- and lineage-specific manner, and moreover along varying degrees of X-inactivation progression. Male blastomeres achieve X-upregulation upon zygotic genome activation while females experience two distinct waves of upregulation, upon imprinted and random X-inactivation; and ablation of Xist impedes female X-upregulation. Female cells carrying two active X chromosomes lack upregulation, yet their collective RNA output exceeds that of a single hyperactive allele. Importantly, this conflicts the conventional dosage compensation model in which naïve female cells are initially subject to biallelic X-upregulation followed by X-inactivation of one allele to correct the X dosage. Together, our study provides key insights to the chain of events of dosage compensation, explaining how transcript copy numbers can remain remarkably stable across developmental windows wherein severe dose imbalance would otherwise be experienced by the cell. The concerted dynamics of X-chromosome upregulation and X-chromosome inactivation, which collectively balance X-chromosome expression, are not well understood. Using allelic single-cell genomics, the authors characterize the dynamics of X-chromosome upregulation and inactivation along mouse embryonic and stem cell development, calling to question keys aspects of the established model of mammalian dosage compensation.

Polycystic ovary syndrome (PCOS) is the main cause of female infertility worldwide and is associated with a substantially increased lifetime risk of comorbidities, including type 2 diabetes mellitus, psychiatric disorders and gynaecological cancers. Despite its high prevalence (~15%) and substantial economic burden, the aetiology of PCOS remains elusive. The genetic loci linked to PCOS so far account for only ~10% of its heritability, which is estimated at 70%. However, growing evidence suggests that altered epigenetic and developmental programming resulting from hormonal dysregulation of the maternal uterine environment contributes to the pathogenesis of PCOS. Male as well as female relatives of women with PCOS are also at an increased risk of developing PCOS-associated reproductive and metabolic disorders. Although PCOS phenotypes are highly heterogenous, hyperandrogenism is thought to be the principal driver of this condition. Current treatments for PCOS are suboptimal as they can only alleviate some of the symptoms; preventative and targeted treatments are sorely needed. This Review presents an overview of the current understanding of the aetiology of PCOS and focuses on the developmental origin and epigenetic inheritance of this syndrome.Polycystic ovary syndrome (PCOS) is a common disorder of unclear aetiology. Although genetic factors contribute to PCOS, they only partially account for its heritability. Here, Stener-Victorin and Deng discuss the evidence that altered epigenetic and developmental programming influence the pathogenesis and familial transmission of PCOS.

If and how obesity and elevated androgens in women with polycystic ovary syndrome (PCOS) affect their offspring’s psychiatric health is unclear. Using data from Swedish population health registers, we showed that daughters of mothers with PCOS have a 78% increased risk of being diagnosed with anxiety disorders. We next generated a PCOS-like mouse (F0) model induced by androgen exposure during late gestation, with or without diet-induced maternal obesity, and showed that the first generation (F1) female offspring develop anxiety-like behavior, which is transgenerationally transmitted through the female germline into the third generation of female offspring (F3) in the androgenized lineage. In contrast, following the male germline, F3 male offspring (mF3) displayed anxiety-like behavior in the androgenized and the obese lineages. Using a targeted approach to search for molecular targets within the amygdala, we identified five differentially expressed genes involved in anxiety-like behavior in F3 females in the androgenized lineage and eight genes in the obese lineage. In mF3 male offspring, three genes were dysregulated in the obese lineage but none in the androgenized lineage. Finally, we performed in vitro fertilization (IVF) using a PCOS mouse model of continuous androgen exposure. We showed that the IVF generated F1 and F2 offspring in the female germline did not develop anxiety-like behavior, while the F2 male offspring (mF2) in the male germline did. Our findings provide evidence that elevated maternal androgens in PCOS and maternal obesity may underlie the risk of a transgenerational transmission of anxiety disorders in children of women with PCOS.

Expression from both alleles is generally observed in analyses of diploid cell populations, but studies addressing allelic expression patterns genome-wide in single cells are lacking. Here, we present global analyses of allelic expression across individual cells of mouse preimplantation embryos of mixed background (CAST/EiJ × C57BL/6J). We discovered abundant (12 to 24%) monoallelic expression of autosomal genes and that expression of the two alieles occurs independently. The monoallelic expression appeared random and dynamic because there was considerable variation among closely related embryonic cells. Similar patterns of monoallelic expression were observed in mature cells. Our allelic expression analysis also demonstrates the de novo inactivation of the paternal × chromosome. We conclude that independent and stochastic allelic transcription generates abundant random monoallelic expression in the mammalian cell.

Laser capture microscopy (LCM) coupled with global transcriptome profiling could enable precise analyses of cell populations without the need for tissue dissociation, but has so far required relatively large numbers of cells. Here we report a robust and highly efficient strategy for LCM coupled with full-length mRNA-sequencing (LCM-seq) developed for single-cell transcriptomics. Fixed cells are subjected to direct lysis without RNA extraction, which both simplifies the experimental procedures as well as lowers technical noise. We apply LCM-seq on neurons isolated from mouse tissues, human post-mortem tissues, and illustrate its utility down to single captured cells. Importantly, we demonstrate that LCM-seq can provide biological insight on highly similar neuronal populations, including motor neurons isolated from different levels of the mouse spinal cord, as well as human midbrain dopamine neurons of the substantia nigra compacta and the ventral tegmental area. Laser capture microscopy (LCM) coupled with global transcriptome profiling requires relatively large numbers of cells. Here, the authors show that LCM coupled with full-length mRNA-sequencing (LCM-seq) can sequence single cells, and that LCM-seq can provide biological insight on highly similar neuronal populations.

Background The comorbidity between polycystic ovary syndrome (PCOS) and obesity has long been observed in clinical settings, but their shared genetic basis remains unclear. Methods Leveraging summary statistics of large-scale GWAS(s) conducted in European-ancestry populations on body mass index (adult BMI, N female =434,794; childhood BMI, N =39,620), waist-to-hip ratio (WHR, N female =381,152), WHR adjusted for BMI (WHR adj BMI, N female =379,501), and PCOS ( N case =10,074, N control =103,164), we performed a large-scale genome-wide cross-trait analysis to quantify overall and local genetic correlation, to identify shared loci, and to infer causal relationship. Results We found positive genetic correlations between PCOS and adult BMI ( r g =0.47, P =2.19×10 −16 ), childhood BMI ( r g =0.31, P =6.72×10 −5 ), and WHR ( r g =0.32, P =1.34×10 −10 ), all withstanding Bonferroni correction. A suggestive significant genetic correlation was found between PCOS and WHR adj BMI ( r g =0.09, P =0.04). Partitioning the whole genome into 1703 nearly independent regions, we observed a significant local genetic correlation for adult BMI and PCOS at chromosome 18: 57630483–59020751. We identified 16 shared loci underlying PCOS and obesity-related traits via cross-trait meta-analysis including 9 loci shared between BMI and PCOS (adult BMI and PCOS: 5 loci; childhood BMI and PCOS: 4 loci), 6 loci shared between WHR and PCOS, and 5 loci shared between WHR adj BMI and PCOS. Mendelian randomization (MR) supported the causal roles of both adult BMI (OR=2.92, 95% CI=2.33–3.67) and childhood BMI (OR=2.76, 95% CI=2.09–3.66) in PCOS, but not WHR (OR=1.19, 95% CI=0.93–1.52) or WHR adj BMI (OR=1.03, 95% CI=0.87–1.22). Genetic predisposition to PCOS did not seem to influence the risk of obesity-related traits. Conclusions Our cross-trait analysis suggests a shared genetic basis underlying obesity and PCOS and provides novel insights into the biological mechanisms underlying these complex traits. Our work informs public health intervention by confirming the important role of weight management in PCOS prevention.

Patch-seq reveals new neuronal subtypes by combining electrophysiological and RNA-seq data on single neurons in situ . Despite the importance of the mammalian neocortex for complex cognitive processes, we still lack a comprehensive description of its cellular components. To improve the classification of neuronal cell types and the functional characterization of single neurons, we present Patch-seq, a method that combines whole-cell electrophysiological patch-clamp recordings, single-cell RNA-sequencing and morphological characterization. Following electrophysiological characterization, cell contents are aspirated through the patch-clamp pipette and prepared for RNA-sequencing. Using this approach, we generate electrophysiological and molecular profiles of 58 neocortical cells and show that gene expression patterns can be used to infer the morphological and physiological properties such as axonal arborization and action potential amplitude of individual neurons. Our results shed light on the molecular underpinnings of neuronal diversity and suggest that Patch-seq can facilitate the classification of cell types in the nervous system.

Type 1 diabetes (T1D) is a chronic autoimmune condition characterized by hyperglycemia resulting from the destruction of insulin-producing β-cells that is traditionally deemed irreversible, but partial remission (PR) with temporary reversal of hyperglycemia is sometimes observed. Here we use single-cell RNA sequencing to delineate the immune cell landscape across patients in different T1D stages. Together with cohort validation and functional assays, we observe dynamic changes in TIGIT + CCR7 − Tregs and CD226 + CCR7 − CD8 + cytotoxic T cells during the peri-remission phase. Machine learning algorithms further identify TIGIT + CCR7 − Tregs and CD226 + CD8 + T cells as biomarkers for β-cell function decline in a predictive model, while cell communication analysis and in vitro assays suggest that TIGIT + CCR7 − Tregs may inhibit CD226 + CCR7 − CD8 + T cells via TGF-β signaling. Lastly, in both cyclophosphamide-induced and streptozotocin (STZ)-induced mouse diabetes models, CD226 inhibition postpones insulitis onset and reduces hyperglycemia severity. Our results thus identify two interrelated immune cell subsets that may serve as biomarkers for monitoring disease progression and targets for therapeutic intervention of T1D. Type 1 diabetes (T1D) manifests as hyperglycemia, with spontaneous remission occasionally observed, but the underneath mechanisms are unclear. Here the authors analyses patients at different stages to find two populations of immune cells correlating with disease progression or remission, while results from mouse diabetes models validate these observations.

Rickard Sandberg and colleagues use allele-sensitive single-cell RNA–seq on primary mouse fibroblasts and human T cells to study clonal and dynamic monoallelic expression patterns. They find that the majority of random monoallelic expression of autosomal genes occurs transiently within individual cells rather than being stably inherited within clonally related cells. Cellular heterogeneity can emerge from the expression of only one parental allele. However, it has remained controversial whether, or to what degree, random monoallelic expression of autosomal genes (aRME) is mitotically inherited (clonal) or stochastic (dynamic) in somatic cells, particularly in vivo . Here we used allele-sensitive single-cell RNA–seq on clonal primary mouse fibroblasts and freshly isolated human CD8 + T cells to dissect clonal and dynamic monoallelic expression patterns. Dynamic aRME affected a considerable portion of the cells' transcriptomes, with levels dependent on the cells' transcriptional activity. Notably, clonal aRME was detected, but it was surprisingly scarce (<1% of genes) and mainly affected the most weakly expressed genes. Consequently, the overwhelming majority of aRME occurs transiently within individual cells, and patterns of aRME are thus primarily scattered throughout somatic cell populations rather than, as previously hypothesized, confined to patches of clonally related cells.