Genetic Identification of Vagal Sensory Neurons That Control Feeding.

Energy homeostasis requires precise measurement of the quantity and quality of ingested food. The vagus nerve innervates the gut and can detect diverse interoceptive cues, but the identity of the key sensory neurons and corresponding signals that regulate food intake remains unknown. Here, we use an approach for target-specific, single-cell RNA sequencing to generate a map of the vagal cell types that innervate the gastrointestinal tract. We show that unique molecular markers identify vagal neurons with distinct innervation patterns, sensory endings, and function. Surprisingly, we find that food intake is most sensitive to stimulation of mechanoreceptors in the intestine, whereas nutrient-activated mucosal afferents have no effect. Peripheral manipulations combined with central recordings reveal that intestinal mechanoreceptors, but not other cell types, potently and durably inhibit hunger-promoting AgRP neurons in the hypothalamus. These findings identify a key role for intestinal mechanoreceptors in the regulation of feeding.

Hair Cell Mechanotransduction Regulates Spontaneous Activity and Spiral Ganglion Subtype Specification in the Auditory System.

Type I spiral ganglion neurons (SGNs) transmit sound information from cochlear hair cells to the CNS. Using transcriptome analysis of thousands of single neurons, we demonstrate that murine type I SGNs consist of subclasses that are defined by the expression of subsets of transcription factors, cell adhesion molecules, ion channels, and neurotransmitter receptors. Subtype specification is initiated prior to the onset of hearing during the time period when auditory circuits mature. Gene mutations linked to deafness that disrupt hair cell mechanotransduction or glutamatergic signaling perturb the firing behavior of SGNs prior to hearing onset and disrupt SGN subtype specification. We thus conclude that an intact hair cell mechanotransduction machinery is critical during the pre-hearing period to regulate the firing behavior of SGNs and their segregation into subtypes. Because deafness is frequently caused by defects in hair cells, our findings have significant ramifications for the etiology of hearing loss and its treatment.

A New Handle for a Hot Topic: Genetic Markers for Warm-Sensing.

Stepping out of an aggressively air-conditioned building into the sweltering heat evokes a number of thermoregulatory responses, both autonomic (sweating) and behavioral (peeling off a layer of clothing or seeking an iced beverage). Just as we come out of the hottest part of the summer, a study by Tan and colleagues provides an exciting breakthrough in our ability to study the neural mechanisms of keeping cool when it's hot.

DNA Phenotyping: Snapshot of a Criminal.

Recent progresses in genomic technologies provide both opportunities and challenges to forensics.

Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.

Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of ∼25,000 BCs, we derived a molecular classification that identified 15 types, including all types observed previously and two novel types, one of which has a non-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.

Mexican Biobank advances population and medical genomics of diverse ancestries.

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data1. To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico at a resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics2-6. We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS7,8. We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide.

Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer.

Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring1. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete2-12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.

DNA N(6)-methyladenine: a new epigenetic mark in eukaryotes?

DNA N(6)-adenine methylation (N(6)-methyladenine; 6mA) in prokaryotes functions primarily in the host defence system. The prevalence and significance of this modification in eukaryotes had been unclear until recently. Here, we discuss recent publications documenting the presence of 6mA in Chlamydomonas reinhardtii, Drosophila melanogaster and Caenorhabditis elegans; consider possible roles for this DNA modification in regulating transcription, the activity of transposable elements and transgenerational epigenetic inheritance; and propose 6mA as a new epigenetic mark in eukaryotes.

Lineage tracing.

Lineage tracing is the identification of all progeny of a single cell. Although its origins date back to developmental biology of invertebrates in the 19(th) century, lineage tracing is now an essential tool for studying stem cell properties in adult mammalian tissues. Lineage tracing provides a powerful means of understanding tissue development, homeostasis, and disease, especially when it is combined with experimental manipulation of signals regulating cell-fate decisions. Recently, the combination of inducible recombinases, multicolor reporter constructs, and live-cell imaging has provided unprecedented insights into stem cell biology. Here we discuss the different experimental strategies currently available for lineage tracing, their associated caveats, and new opportunities to integrate lineage tracing with the monitoring of intracellular signaling pathways.

Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase.

During cellular reprogramming, only a small fraction of cells become induced pluripotent stem cells (iPSCs). Previous analyses of gene expression during reprogramming were based on populations of cells, impeding single-cell level identification of reprogramming events. We utilized two gene expression technologies to profile 48 genes in single cells at various stages during the reprogramming process. Analysis of early stages revealed considerable variation in gene expression between cells in contrast to late stages. Expression of Esrrb, Utf1, Lin28, and Dppa2 is a better predictor for cells to progress into iPSCs than expression of the previously suggested reprogramming markers Fbxo15, Fgf4, and Oct4. Stochastic gene expression early in reprogramming is followed by a late hierarchical phase with Sox2 being the upstream factor in a gene expression hierarchy. Finally, downstream factors derived from the late phase, which do not include Oct4, Sox2, Klf4, c-Myc, and Nanog, can activate the pluripotency circuitry.

Integrated spatial genomics reveals global architecture of single nuclei.

Identifying the relationships between chromosome structures, nuclear bodies, chromatin states and gene expression is an overarching goal of nuclear-organization studies1-4. Because individual cells appear to be highly variable at all these levels5, it is essential to map different modalities in the same cells. Here we report the imaging of 3,660 chromosomal loci in single mouse embryonic stem (ES) cells using DNA seqFISH+, along with 17 chromatin marks and subnuclear structures by sequential immunofluorescence and the expression profile of 70 RNAs. Many loci were invariably associated with immunofluorescence marks in single mouse ES cells. These loci form 'fixed points' in the nuclear organizations of single cells and often appear on the surfaces of nuclear bodies and zones defined by combinatorial chromatin marks. Furthermore, highly expressed genes appear to be pre-positioned to active nuclear zones, independent of bursting dynamics in single cells. Our analysis also uncovered several distinct mouse ES cell subpopulations with characteristic combinatorial chromatin states. Using clonal analysis, we show that the global levels of some chromatin marks, such as H3 trimethylation at lysine 27 (H3K27me3) and macroH2A1 (mH2A1), are heritable over at least 3-4 generations, whereas other marks fluctuate on a faster time scale. This seqFISH+-based spatial multimodal approach can be used to explore nuclear organization and cell states in diverse biological systems.

GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

The National Lung Matrix Trial of personalized therapy in lung cancer.

The majority of targeted therapies for non-small-cell lung cancer (NSCLC) are directed against oncogenic drivers that are more prevalent in patients with light exposure to tobacco smoke1-3. As this group represents around 20% of all patients with lung cancer, the discovery of stratified medicine options for tobacco-associated NSCLC is a high priority. Umbrella trials seek to streamline the investigation of genotype-based treatments by screening tumours for multiple genomic alterations and triaging patients to one of several genotype-matched therapeutic agents. Here we report the current outcomes of 19 drug-biomarker cohorts from the ongoing National Lung Matrix Trial, the largest umbrella trial in NSCLC. We use next-generation sequencing to match patients to appropriate targeted therapies on the basis of their tumour genotype. The Bayesian trial design enables outcome data from open cohorts that are still recruiting to be reported alongside data from closed cohorts. Of the 5,467 patients that were screened, 2,007 were molecularly eligible for entry into the trial, and 302 entered the trial to receive genotype-matched therapy-including 14 that re-registered to the trial for a sequential trial drug. Despite pre-clinical data supporting the drug-biomarker combinations, current evidence shows that a limited number of combinations demonstrate clinically relevant benefits, which remain concentrated in patients with lung cancers that are associated with minimal exposure to tobacco smoke.

Ancient West African foragers in the context of African population history.

Our knowledge of ancient human population structure in sub-Saharan Africa, particularly prior to the advent of food production, remains limited. Here we report genome-wide DNA data from four children-two of whom were buried approximately 8,000 years ago and two 3,000 years ago-from Shum Laka (Cameroon), one of the earliest known archaeological sites within the probable homeland of the Bantu language group1-11. One individual carried the deeply divergent Y chromosome haplogroup A00, which today is found almost exclusively in the same region12,13. However, the genome-wide ancestry profiles of all four individuals are most similar to those of present-day hunter-gatherers from western Central Africa, which implies that populations in western Cameroon today-as well as speakers of Bantu languages from across the continent-are not descended substantially from the population represented by these four people. We infer an Africa-wide phylogeny that features widespread admixture and three prominent radiations, including one that gave rise to at least four major lineages deep in the history of modern humans.

snpAIMeR: R package for evaluating ancestry informative marker contributions in non-model population diagnostics.

MOTIVATION: Single nucleotide polymorphism (SNP) markers are increasingly popular for population genomics and inferring ancestry for individuals of unknown origin. Because large SNP datasets are impractical for rapid and routine analysis, diagnostics rely on panels of highly informative markers. Strategies exist for selecting these markers, however, resources for efficiently evaluating their performance are limited for non-model systems. RESULTS: snpAIMeR is a user-friendly R package that evaluates the efficacy of genomic markers for the cluster assignment of unknown individuals. It is intended to help minimize panel size and genotyping effort by determining the informativeness of candidate diagnostic markers. Provided genotype data from individuals of known origin, it uses leave-one-out cross-validation to determine population assignment rates for individual markers and marker combinations. AVAILABILITY AND IMPLEMENTATION: snpAIMeR is available on CRAN (https://CRAN.R-project.org/package=snpAIMeR).

Novel ferroptosis gene biomarkers and immune infiltration profiles in diabetic kidney disease via bioinformatics.

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, exhibiting high disability and mortality rates. Ferroptosis is vital for the progression of DKD, but the exact mechanism remains unclear. This study aimed to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with the immune and to identify new diagnostic biomarkers to help treat and diagnose DKD. GSE30122 and GSE47185 were obtained from the Gene Expression Omnibus database and were integrated into a merged dataset, followed by functional enrichment analysis. Then potential differentially expressed genes were screened. Ferroptosis-related differentially-expressed genes were identified, followed by gene ontology analysis. Protein-protein interaction networks were constructed and hub genes were screened. The immune cell-infiltrating state in the dataset was assessed using appropriate algorithms. Immune signature subtypes were constructed using the consensus clustering analysis. Hub gene expression was validated using qRT-PCR and immunohistochemistry. A total of Eleven screened ferroptosis-related differentially expressed genes were screened. Six potentially diagnostically favorable ferroptosis-related hub genes were identified. Significantly increased expression of γδT cells, resting mast cells, and macrophages infiltration was observed in the DKD group. Additionally, two distinct immune signature subgroups were identified. Ferroptosis-related hub genes were significantly correlated with differentially infiltrated immune cells. Six hub genes were significantly upregulated in HK-2 cells following high glucose treatment and in human kidney tissues of patients with DKD. Six ferroptosis-related hub genes were identified as potential biomarkers of diabetic kidney disease, but further validation is needed.

Genetic predisposition to mosaic Y chromosome loss in blood.

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism1-5, yet our knowledge of the causes and consequences of this is limited. Here, using a computational approach, we estimate that 20% of the male population represented in the UK Biobank study (n = 205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes that are involved in cell-cycle regulation and cancer susceptibility, as well as somatic drivers of tumour growth and targets of cancer therapy. We demonstrate that genetic susceptibility to LOY is associated with non-haematological effects on health in both men and women, which supports the hypothesis that clonal haematopoiesis is a biomarker of genomic instability in other tissues. Single-cell RNA sequencing identifies dysregulated expression of autosomal genes in leukocytes with LOY and provides insights into why clonal expansion of these cells may occur. Collectively, these data highlight the value of studying clonal mosaicism to uncover fundamental mechanisms that underlie cancer and other ageing-related diseases.

The single-cell transcriptional landscape of mammalian organogenesis.

Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting 'mouse organogenesis cell atlas' (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.

ImmCluster: an ensemble resource for immunology cell type clustering and annotations in normal and cancerous tissues.

Single-cell transcriptome has enabled the transcriptional profiling of thousands of immune cells in complex tissues and cancers. However, subtle transcriptomic differences in immune cell subpopulations and the high dimensionality of transcriptomic data make the clustering and annotation of immune cells challenging. Herein, we introduce ImmCluster (http://bio-bigdata.hrbmu.edu.cn/ImmCluster) for immunology cell type clustering and annotation. We manually curated 346 well-known marker genes from 1163 studies. ImmCluster integrates over 420 000 immune cells from nine healthy tissues and over 648 000 cells from different tumour samples of 17 cancer types to generate stable marker-gene sets and develop context-specific immunology references. In addition, ImmCluster provides cell clustering using seven reference-based and four marker gene-based computational methods, and the ensemble method was developed to provide consistent cell clustering than individual methods. Five major analytic modules were provided for interactively exploring the annotations of immune cells, including clustering and annotating immune cell clusters, gene expression of markers, functional assignment in cancer hallmarks, cell states and immune pathways, cell-cell communications and the corresponding ligand-receptor interactions, as well as online tools. ImmCluster generates diverse plots and tables, enabling users to identify significant associations in immune cell clusters simultaneously. ImmCluster is a valuable resource for analysing cellular heterogeneity in cancer microenvironments.

zMADM (zebrafish mosaic analysis with double markers) for single-cell gene knockout and dual-lineage tracing.

As a vertebrate model organism, zebrafish has many unique advantages in developmental studies, regenerative biology, and disease modeling. However, tissue-specific gene knockout in zebrafish is challenging due to technical difficulties in making floxed alleles. Even when successful, tissue-level knockout can affect too many cells, making it difficult to distinguish cell autonomous from noncell autonomous gene function. Here, we present a genetic system termed zebrafish mosaic analysis with double markers (zMADM). Through Cre/loxP-mediated interchromosomal mitotic recombination of two reciprocally chimeric fluorescent genes, zMADM generates sporadic (<0.5%), GFP+ mutant cells along with RFP+ sibling wild-type cells, enabling phenotypic analysis at single-cell resolution. Using wild-type zMADM, we traced two sibling cells (GFP+ and RFP+) in real time during a dynamic developmental process. Using nf1 mutant zMADM, we demonstrated an overproliferation phenotype of nf1 mutant cells in comparison to wild-type sibling cells in the same zebrafish. The readiness of zMADM to produce sporadic mutant cells without the need to generate floxed alleles should fundamentally improve the throughput of genetic analysis in zebrafish; the lineage-tracing capability combined with phenotypic analysis at the single-cell level should lead to deep insights into developmental and disease mechanisms. Therefore, we are confident that zMADM will enable groundbreaking discoveries once broadly distributed in the field.