The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution

Cell type-specific gene expression dynamics during human brain maturation

The human brain undergoes protracted post-natal maturation, guided by dynamic changes in gene expression. Most studies exploring these processes have used bulk tissue analyses, which mask cell type-specific gene expression dynamics. Here, using single nucleus (sn)RNA-seq on temporal lobe tissue, including samples of African ancestry, we build a joint paediatric and adult atlas of 75 cell subtypes, which we verify with spatial transcriptomics. We explore the differences between paediatric and adult cell types, revealing the genes and pathways that change during brain maturation. Our results highlight excitatory neuron subtypes, including the LTK and FREM subtypes, that show elevated expression of genes associated with cognition and synaptic plasticity in paediatric tissue. The new resources we present here improve our understanding of the brain during its development and contribute to global efforts to build an inclusive brain cell map.

How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research

Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.

Highlights of how single-cell analyses are illuminating differentiation and disease in the gastric corpus

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique to identify novel cell markers, developmental trajectories, and transcriptional changes during cell differentiation and disease onset and progression. In this review, we highlight recent scRNA-seq studies of the gastric corpus in both human and murine systems that have provided insight into gastric organogenesis, identified novel markers for the various gastric lineages during development and in adults, and revealed transcriptional changes during regeneration and tumorigenesis. Overall, by elucidating transcriptional states and fluctuations at the cellular level in healthy and disease contexts, scRNA-seq may lead to better, more personalized clinical treatments for disease progression.

Genetics of human brain development

Brain development in humans is achieved through precise spatiotemporal genetic control, the mechanisms of which remain largely elusive. Recently, integration of technological advances in human stem cell-based modelling with genome editing has emerged as a powerful platform to establish causative links between genotypes and phenotypes directly in the human system. Here, we review our current knowledge of complex genetic regulation of each key step of human brain development through the lens of evolutionary specialization and neurodevelopmental disorders and highlight the use of human stem cell-derived 2D cultures and 3D brain organoids to investigate human-enriched features and disease mechanisms. We also discuss opportunities and challenges of integrating new technologies to reveal the genetic architecture of human brain development and disorders.

A tridimensional atlas of the developing human head

The evolution and development of the head have long captivated researchers due to the crucial role of the head as the gateway for sensory stimuli and the intricate structural complexity of the head. Although significant progress has been made in understanding head development in various vertebrate species, our knowledge of early human head ontogeny remains limited. Here, we used advanced whole-mount immunostaining and 3D imaging techniques to generate a comprehensive 3D cellular atlas of human head embryogenesis. We present detailed developmental series of diverse head tissues and cell types, including muscles, vasculature, cartilage, peripheral nerves, and exocrine glands. These datasets, accessible through a dedicated web interface, provide insights into human embryogenesis. We offer perspectives on the branching morphogenesis of human exocrine glands and unknown features of the development of neurovascular and skeletomuscular structures. These insights into human embryology have important implications for understanding craniofacial defects and neurological disorders and advancing diagnostic and therapeutic strategies.

New Challenges for Anatomists in the Era of Omics

Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.

Open Data in the Era of the GDPR: Lessons from the Human Cell Atlas

The Human Cell Atlas (HCA) is striving to build an open community that is inclusive of all researchers adhering to its principles and as open as possible with respect to data access and use. However, open data sharing can pose certain challenges. For instance, being a global initiative, the HCA must contend with a patchwork of local and regional privacy rules. A notable example is the implementation of the European Union General Data Protection Regulation (GDPR), which caused some concern in the biomedical and genomic data-sharing community. We examine how the HCA's large, international group of researchers is investing tremendous efforts into ensuring appropriate sharing of data. We describe the HCA's objectives and governance, how it defines open data sharing, and ethico-legal challenges encountered early in its development; in particular, we describe the challenges prompted by the GDPR. Finally, we broaden the discussion to address tools and strategies that can be used to address ethical data governance.

Generative modeling of single-cell gene expression for dose-dependent chemical perturbations

Single-cell sequencing reveals the heterogeneity of cellular response to chemical perturbations. However, testing all relevant combinations of cell types, chemicals, and doses is a daunting task. A deep generative learning formalism called variational autoencoders (VAEs) has been effective in predicting single-cell gene expression perturbations for single doses. Here, we introduce single-cell variational inference of dose-response (scVIDR), a VAE-based model that predicts both single-dose and multiple-dose cellular responses better than existing models. We show that scVIDR can predict dose-dependent gene expression across mouse hepatocytes, human blood cells, and cancer cell lines. We biologically interpret the latent space of scVIDR using a regression model and use scVIDR to order individual cells based on their sensitivity to chemical perturbation by assigning each cell a "pseudo-dose" value. We envision that scVIDR can help reduce the need for repeated animal testing across tissues, chemicals, and doses.

Systematic Methods for Isolating High Purity Nuclei from Ten Important Plants for Omics Interrogation

Recent advances in developmental biology have been made possible by using multi-omic studies at single cell resolution. However, progress in plants has been slowed, owing to the tremendous difficulty in protoplast isolation from most plant tissues and/or oversize protoplasts during flow cytometry purification. Surprisingly, rapid innovations in nucleus research have shed light on plant studies in single cell resolution, which necessitates high quality and efficient nucleus isolation. Herein, we present efficient nuclei isolation protocols from the leaves of ten important plants including Arabidopsis, rice, maize, tomato, soybean, banana, grape, citrus, apple, and litchi. We provide a detailed procedure for nucleus isolation, flow cytometry purification, and absolute nucleus number quantification. The nucleus isolation buffer formula of the ten plants tested was optimized, and the results indicated a high nuclei yield. Microscope observations revealed high purity after flow cytometry sorting, and the DNA and RNA quality extract from isolated nuclei were monitored by using the nuclei in cell division cycle and single nucleus RNA sequencing (snRNA-seq) studies, with detailed procedures provided. The findings indicated that nucleus yield and quality meet the requirements of snRNA-seq, cell division cycle, and likely other omic studies. The protocol outlined here makes it feasible to perform plant omic studies at single cell resolution.

Approaches to benchmark and characterize in vitro human model systems

In vitro human models, such as gastruloids and organoids, are complex three-dimensional (3D) structures often consist of cells from multiple germ layers that possess some attributes of a developing embryo or organ. To use these models to interrogate human development and organogenesis, these 3D models must accurately recapitulate aspects of their in vivo counterparts. Recent advances in single-cell technologies, including sequencing and spatial approaches, have enabled efforts to better understand and directly compare organoids with native tissues. For example, single-cell genomic efforts have created cell and organ atlases that enable benchmarking of in vitro models and can also be leveraged to gain novel biological insights that can be used to further improve in vitro models. This Spotlight discusses the state of current in vitro model systems, the efforts to create large publicly available atlases of the developing human and how these data are being used to improve organoids. Limitations and perspectives on future efforts are also discussed.

Neuromuscular disorders: finding the missing genetic diagnoses

Neuromuscular disorders (NMDs) are a wide-ranging group of diseases that seriously affect the quality of life of affected individuals. The development of next-generation sequencing revolutionized the diagnosis of NMD, enabling the discovery of hundreds of NMD genes and many more pathogenic variants. However, the diagnostic yield of genetic testing in NMD cohorts remains incomplete, indicating a large number of genetic diagnoses are not identified through current methods. Fortunately, recent advancements in sequencing technologies, analytical tools, and high-throughput functional screening provide an opportunity to circumvent current challenges. Here, we discuss reasons for missing genetic diagnoses in NMD, how emerging technologies and tools can overcome these hurdles, and examine future approaches to improving diagnostic yields in NMD.

Leveraging Systems Immunology to Optimize Diagnosis and Treatment of Inborn Errors of Immunity

Inborn errors of immunity (IEI) are monogenic disorders that can cause diverse symptoms, including recurrent infections, autoimmunity and malignancy. While many factors have contributed, the increased availability of next-generation sequencing has been central in the remarkable increase in identification of novel monogenic IEI over the past years. Throughout this phase of disease discovery, it has also become evident that a given gene variant does not always yield a consistent phenotype, while variants in seemingly disparate genes can lead to similar clinical presentations. Thus, it is increasingly clear that the clinical phenotype of an IEI patient is not defined by genetics alone, but is also impacted by a myriad of factors. Accordingly, we need methods to amplify our current diagnostic algorithms to better understand mechanisms underlying the variability in our patients and to optimize treatment. In this review, we will explore how systems immunology can contribute to optimizing both diagnosis and treatment of IEI patients by focusing on identifying and quantifying key dysregulated pathways. To improve mechanistic understanding in IEI we must deeply evaluate our rare IEI patients using multimodal strategies, allowing both the quantification of altered immune cell subsets and their functional evaluation. By studying representative controls and patients, we can identify causative pathways underlying immune cell dysfunction and move towards functional diagnosis. Attaining this deeper understanding of IEI will require a stepwise strategy. First, we need to broadly apply these methods to IEI patients to identify patterns of dysfunction. Next, using multimodal data analysis, we can identify key dysregulated pathways. Then, we must develop a core group of simple, effective functional tests that target those pathways to increase efficiency of initial diagnostic investigations, provide evidence for therapeutic selection and contribute to the mechanistic evaluation of genetic results. This core group of simple, effective functional tests, targeting key pathways, can then be equitably provided to our rare patients. Systems biology is thus poised to reframe IEI diagnosis and therapy, fostering research today that will provide streamlined diagnosis and treatment choices for our rare and complex patients in the future, as well as providing a better understanding of basic immunology.

Centers for Mendelian Genomics: A decade of facilitating gene discovery

PURPOSE

Mendelian disease genomic research has undergone a massive transformation over the past decade. With increasing availability of exome and genome sequencing, the role of Mendelian research has expanded beyond data collection, sequencing, and analysis to worldwide data sharing and collaboration.

METHODS

Over the past 10 years, the National Institutes of Health-supported Centers for Mendelian Genomics (CMGs) have played a major role in this research and clinical evolution.

RESULTS

We highlight the cumulative gene discoveries facilitated by the program, biomedical research leveraged by the approach, and the larger impact on the research community. Beyond generating a list of gene-phenotype relationships and participating in widespread data sharing, the CMGs have created resources, tools, and training for the larger community to foster understanding of genes and genome variation. The CMGs have participated in a wide range of data sharing activities, including deposition of all eligible CMG data into the Analysis, Visualization, and Informatics Lab-space (AnVIL), sharing candidate genes through the Matchmaker Exchange and the CMG website, and sharing variants in Genotypes to Mendelian Phenotypes (Geno2MP) and VariantMatcher.

CONCLUSION

The work is far from complete; strengthening communication between research and clinical realms, continued development and sharing of knowledge and tools, and improving access to richly characterized data sets are all required to diagnose the remaining molecularly undiagnosed patients.

Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain

A key aspect of nearly all single-cell sequencing experiments is dissociation of intact tissues into single-cell suspensions. While many protocols have been optimized for optimal cell yield, they have often overlooked the effects that dissociation can have on ex vivo gene expression. Here, we demonstrate that use of enzymatic dissociation on brain tissue induces an aberrant ex vivo gene expression signature, most prominently in microglia, which is prevalent in published literature and can substantially confound downstream analyses. To address this issue, we present a rigorously validated protocol that preserves both in vivo transcriptional profiles and cell-type diversity and yield across tissue types and species. We also identify a similar signature in postmortem human brain single-nucleus RNA-sequencing datasets, and show that this signature is induced in freshly isolated human tissue by exposure to elevated temperatures ex vivo. Together, our results provide a methodological solution for preventing artifactual gene expression changes during fresh tissue digestion and a reference for future deeper analysis of the potential confounding states present in postmortem human samples.

The immune landscape of SARS-CoV-2-associated Multisystem Inflammatory Syndrome in Children (MIS-C) from acute disease to recovery

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening disease occurring several weeks after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Deep immune profiling showed acute MIS-C patients had highly activated neutrophils, classical monocytes and memory CD8+ T-cells, with increased frequencies of B-cell plasmablasts and double-negative B-cells. Post treatment samples from the same patients, taken during symptom resolution, identified recovery-associated immune features including increased monocyte CD163 levels, emergence of a new population of immature neutrophils and, in some patients, transiently increased plasma arginase. Plasma profiling identified multiple features shared by MIS-C, Kawasaki Disease and COVID-19 and that therapeutic inhibition of IL-6 may be preferable to IL-1 or TNF-α. We identified several potential mechanisms of action for IVIG, the most commonly used drug to treat MIS-C. Finally, we showed systemic complement activation with high plasma C5b-9 levels is common in MIS-C suggesting complement inhibitors could be used to treat the disease.

Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design

BACKGROUND

Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower library preparation costs and reduced unwanted technological variation, such as batch effects. Computational demultiplexing tools based on natural genetic variation between individuals provide a simple approach to demultiplex samples, which does not require complex additional experimental procedures. However, to our knowledge these tools have not been evaluated in cancer, where somatic variants, which could differ between cells from the same sample, may obscure the signal in natural genetic variation.

RESULTS

Here, we performed in silico benchmark evaluations by combining raw sequencing reads from multiple single-cell samples in high-grade serous ovarian cancer, which has a high copy number burden, and lung adenocarcinoma, which has a high tumor mutational burden. Our results confirm that genetic demultiplexing tools can be effectively deployed on cancer tissue using a pooled experimental design, although high proportions of ambient RNA from cell debris reduce performance.

CONCLUSIONS

This strategy provides significant cost savings through pooled library preparation. To facilitate similar analyses at the experimental design phase, we provide freely accessible code and a reproducible Snakemake workflow built around the best-performing tools found in our in silico benchmark evaluations, available at https://github.com/lmweber/snp-dmx-cancer.

A roadmap for the Human Developmental Cell Atlas

The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.

Single cell transcriptome research in human placenta

Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications, such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.

Rational Small Molecule Treatment for Genetic Epilepsies

Genetic testing has yielded major advances in our understanding of the causes of epilepsy. Seizures remain resistant to treatment in a significant proportion of cases, particularly in severe, childhood-onset epilepsy, the patient population in which an underlying causative genetic variant is most likely to be identified. A genetic diagnosis can be explanatory as to etiology, and, in some cases, might suggest a therapeutic approach; yet, a clear path from genetic diagnosis to treatment remains unclear in most cases. Here, we discuss theoretical considerations behind the attempted use of small molecules for the treatment of genetic epilepsies, which is but one among various approaches currently under development. We explore a few salient examples and consider the future of the small molecule approach for genetic epilepsies. We conclude that significant additional work is required to understand how genetic variation leads to dysfunction of epilepsy-associated protein targets, and how this impacts the function of diverse subtypes of neurons embedded within distributed brain circuits to yield epilepsy and epilepsy-associated comorbidities. A syndrome- or even variant-specific approach may be required to achieve progress. Advances in the field will require improved methods for large-scale target validation, compound identification and optimization, and the development of accurate model systems that reflect the core features of human epilepsy syndromes, as well as novel approaches towards clinical trials of such compounds in small rare disease cohorts.

Charting human development using a multi-endodermal organ atlas and organoid models

Organs are composed of diverse cell types that traverse transient states during organogenesis. To interrogate this diversity during human development, we generate a single-cell transcriptome atlas from multiple developing endodermal organs of the respiratory and gastrointestinal tract. We illuminate cell states, transcription factors, and organ-specific epithelial stem cell and mesenchyme interactions across lineages. We implement the atlas as a high-dimensional search space to benchmark human pluripotent stem cell (hPSC)-derived intestinal organoids (HIOs) under multiple culture conditions. We show that HIOs recapitulate reference cell states and use HIOs to reconstruct the molecular dynamics of intestinal epithelium and mesenchyme emergence. We show that the mesenchyme-derived niche cue NRG1 enhances intestinal stem cell maturation in vitro and that the homeobox transcription factor CDX2 is required for regionalization of intestinal epithelium and mesenchyme in humans. This work combines cell atlases and organoid technologies to understand how human organ development is orchestrated.

How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data

Rare diseases, although individually rare, collectively affect approximately 350 million people worldwide. Currently, nearly 6,000 distinct rare disorders with a known molecular basis have been described, yet establishing a specific diagnosis based on the clinical phenotype is challenging. Increasing integration of whole exome sequencing into routine diagnostics of rare diseases is improving diagnostic rates. Nevertheless, about half of the patients do not receive a genetic diagnosis due to the challenges of variant detection and interpretation. During the last years, RNA sequencing is increasingly used as a complementary diagnostic tool providing functional data. Initially, arbitrary thresholds have been applied to call aberrant expression, aberrant splicing, and mono-allelic expression. With the application of RNA sequencing to search for the molecular diagnosis, the implementation of robust statistical models on normalized read counts allowed for the detection of significant outliers corrected for multiple testing. More recently, machine learning methods have been developed to improve the normalization of RNA sequencing read count data by taking confounders into account. Together the methods have increased the power and sensitivity of detection and interpretation of pathogenic variants, leading to diagnostic rates of 10-35% in rare diseases. In this review, we provide an overview of the methods used for RNA sequencing and illustrate how these can improve the diagnostic yield of rare diseases.

Data-driven approaches to advance research and clinical care for pediatric cancer

Pediatric cancer is a rare disease with a distinct etiology and mutational landscape compared with adult cancer. Multi-omics profiling of retrospective and prospective cohorts coupled with innovative computational analysis have been instrumental in uncovering mechanisms of tumorigenesis and drug resistance that are now informing pediatric cancer clinical therapy. In this review we present the major data resources of pediatric cancer and actionable insights into pediatric cancer etiology stemming from the identification of oncogenic gene fusions, mutational signature analysis, systems biology, cancer predisposition and survivorship studies - that have led to improved clinical diagnosis, discovery of new drug-targets, pharmacological therapy, and screening for genetic predisposition. Ultimately, integration of large-scale omics datasets generated through international collaboration is required to maximize the power of data-driven approaches to advance pediatric cancer research informing clinical therapy.

A robust method of nuclei isolation for single-cell RNA sequencing of solid tissues from the plant genus Populus

Single-cell transcriptome analysis has been extensively applied in humans and animal models to uncover gene expression heterogeneity between the different cell types of a tissue or an organ. It demonstrated its capability to discover key regulatory elements that determine cell fate during developmental programs. Single-cell analysis requires the isolation and labeling of the messenger RNA (mRNA) derived from each cell. These challenges were primarily addressed in mammals by developing microfluidic-based approaches. For plant species whose cells contain cell walls, these approaches have generally required the generation of isolated protoplasts. Many plant tissues' secondary cell wall hinders enzymatic digestion required for individual protoplast isolation, resulting in an unequal representation of cell types in a protoplast population. This limitation is especially critical for cell types located in the inner layers of a tissue or the inner tissues of an organ. Consequently, single-cell RNA sequencing (scRNA-seq) studies using microfluidic approaches in plants have mainly been restricted to Arabidopsis roots, for which well-established procedures of protoplast isolation are available. Here we present a simple alternative approach to generating high-quality protoplasts from plant tissue by characterizing the mRNA extracted from individual nuclei instead of whole cells. We developed the protocol using two different plant materials with varying cellular complexity levels and cell wall structure, Populus shoot apices, and more lignified stems. Using the 10× Genomics Chromium technology, we show that this procedure results in intact mRNA isolation and limited leakage, with a broad representation of individual cell transcriptomes.

Selective Modulation of a Pan-Essential Protein as a Therapeutic Strategy in Cancer

Cancer dependency maps, which use CRISPR/Cas9 depletion screens to profile the landscape of genetic dependencies in hundreds of cancer cell lines, have identified context-specific dependencies that could be therapeutically exploited. An ideal therapy is both lethal and precise, but these depletion screens cannot readily distinguish between gene effects that are cytostatic or cytotoxic. Here, we use a diverse panel of functional genomic screening assays to identify NXT1 as a selective and rapidly lethal in vivo relevant genetic dependency in MYCN-amplified neuroblastoma. NXT1 heterodimerizes with NXF1, and together they form the principal mRNA nuclear export machinery. We describe a previously unrecognized mechanism of synthetic lethality between NXT1 and its paralog NXT2: their common essential binding partner NXF1 is lost only in the absence of both. We propose a potential therapeutic strategy for tumor-selective elimination of a protein that, if targeted directly, is expected to cause widespread toxicity. SIGNIFICANCE: We provide a framework for identifying new therapeutic targets from functional genomic screens. We nominate NXT1 as a selective lethal target in neuroblastoma and propose a therapeutic approach where the essential protein NXF1 can be selectively eliminated in tumor cells by exploiting the NXT1-NXT2 paralog relationship.See related commentary by Wang and Abdel-Wahab, p. 2129.This article is highlighted in the In This Issue feature, p. 2113.

Coupling of autism genes to tissue-wide expression and dysfunction of synapse, calcium signalling and transcriptional regulation

Autism Spectrum Disorder (ASD) is a heterogeneous disorder that is often accompanied with many co-morbidities. Recent genetic studies have identified various pathways from hundreds of candidate risk genes with varying levels of association to ASD. However, it is unknown which pathways are specific to the core symptoms or which are shared by the co-morbidities. We hypothesised that critical ASD candidates should appear widely across different scoring systems, and that comorbidity pathways should be constituted by genes expressed in the relevant tissues. We analysed the Simons Foundation for Autism Research Initiative (SFARI) database and four independently published scoring systems and identified 292 overlapping genes. We examined their mRNA expression using the Genotype-Tissue Expression (GTEx) database and validated protein expression levels using the human protein atlas (HPA) dataset. This led to clustering of the overlapping ASD genes into 2 groups; one with 91 genes primarily expressed in the central nervous system (CNS geneset) and another with 201 genes expressed in both CNS and peripheral tissues (CNS+PT geneset). Bioinformatic analyses showed a high enrichment of CNS development and synaptic transmission in the CNS geneset, and an enrichment of synapse, chromatin remodelling, gene regulation and endocrine signalling in the CNS+PT geneset. Calcium signalling and the glutamatergic synapse were found to be highly interconnected among pathways in the combined geneset. Our analyses demonstrate that 2/3 of ASD genes are expressed beyond the brain, which may impact peripheral function and involve in ASD co-morbidities, and relevant pathways may be explored for the treatment of ASD co-morbidities.

No population left behind: Improving paediatric drug safety using informatics and systems biology

Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age-dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data-driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.

Is highly expressed ACE 2 in pregnant women "a curse" in times of COVID-19 pandemic?

Angiotensin-converting enzyme 2 (ACE 2) is a membrane-bound enzyme that cleaves angiotensin II (Ang II) into angiotensin (1-7). It also serves as an important binding site for SARS-CoV-2, thereby, facilitating viral entry into target host cells. ACE 2 is abundantly present in the intestine, kidney, heart, lungs, and fetal tissues. Fetal ACE 2 is involved in myocardium growth, lungs and brain development. ACE 2 is highly expressed in pregnant women to compensate preeclampsia by modulating angiotensin (1-7) which binds to the Mas receptor, having vasodilator action and maintain fluid homeostasis. There are reports available on Zika, H1N1 and SARS-CoV where these viruses have shown to produce fetal defects but very little is known about SARS-CoV-2 involvement in pregnancy, but it might have the potential to interact with fetal ACE 2 and enhance COVID-19 transmission to the fetus, leading to fetal morbidity and mortality. This review sheds light on a path of SARS-CoV-2 transmission risk in pregnancy and its possible link with fetal ACE 2.

Ethical, Legal, and Social Issues (ELSI) of Responsible Data Sharing Involving Children in Genomics: A Systematic Literature Review of Reasons

BACKGROUND

Progress in precision medicine relies on the access to, use of, and exchange of genomic and associated clinical data, including from children. The ethical, legal, and social issues (ELSI) of such data access, use, and exchange may be accentuated in the pediatric context due in part to the highly sensitive nature of genomic data, children's consent-related vulnerabilities, and uncertain risks of reidentification. Systematic analyses of the ELSI and scientific reasons for why and how genomic data may be shared responsibly are, however, limited. Methods: We conducted a modified systematic review of reasons according to Sofaer and Strech to examine the ELSI and scientific reasons for "responsible" sharing of children's genomic and associated clinical data. Empirical articles, commentaries, and data-sharing policies indexed in Medline, Scopus, Web of Science, and BIOSIS were included in the analysis if they discussed ELSI and were published between 2003 and 2017 in English. Results: One hundred and fifty-one records met our inclusion criteria. We identified 11 unique reasons and 8 subreasons for why children's genomic data should or should not be shared. Enhancing the prospect of direct and indirect benefits and maximizing the utility of children's data were top reasons why data should be shared. Inadequate data privacy protection was the leading reason why it should not. We furthermore identified 8 reasons and 30 subreasons that support conditional data sharing, in which recontact for the continued use of children's data once they reach the age of majority was the most frequently endorsed condition. Conclusions: The complete list of ELSI reasons and responsible conditions provides an evidentiary basis upon which institutions can develop data-sharing policies. Institutions should encourage the sharing of children's data to advance genomic research, while heeding special reconsent and data protection mechanisms that may help mitigate uncertain longitudinal risks for children and families.

Iranian kidney transplant recipients with COVID-19 infection: Clinical outcomes and cytomegalovirus coinfection

BACKGROUND

There is a high risk of COVID-19 in kidney transplant recipients (KTRs) because of chronic immunosuppression and severe cytomegalovirus (CMV) pneumonitis.

CASE PRESENTATION

A case series of 10 KTRs with COVID-19 in Iran was developed. Participants consisted of two female and eight male patients, aged 46-68 years old. The data related to clinical laboratory tests, outcomes, diagnosis, and drug treatments were collected. The RT-PCR confirmed the COVID-19 infection in KTRs. The assessment of serum biochemical and blood hematological factors showed that there was a strong correlation between COVID-19 intensity and high serum Cr, BUN, and ALT levels, high CRP concentration, and lower lymphocyte and platelet counts in male KTRs. Ground-glass opacity (GGO) was the main radiologic pattern visible on both chest radiographs of computed tomography scans. The COVID-19 and CMV coinfection in KTRs resulted in large-size kidneys with severe parenchymal echogenicity and hydronephrosis. The combined use of effective antibiotic and antiviral drugs was suitable to prevent COVID-19 progression in KTRs.

CONCLUSIONS

The coincidence of COVID-19 and CMV in KTRs may potentially increase the mortality risk of patients. The levels of Cr, BUN, ALT, and CRP as well as lymphocytes count in these patients should be continuously controlled.

Developmental Pharmacodynamics and Modeling in Pediatric Drug Development

Challenges in pediatric drug development include small patient numbers, limited outcomes research, ethical barriers, and sparse biosamples. Increasingly, pediatric drug development is focusing on extrapolation: leveraging knowledge about adult disease and drug responses to inform projections of drug and clinical trial performance in pediatric subpopulations. Pharmacokinetic-pharmacodynamic (PK-PD) modeling and extrapolation aim to reduce the numbers of patients and data points needed to establish efficacy. Planning for PK-PD and biomarker studies should begin early in the adult drug development program. Extrapolation relies on the assumption that both the underlying disease and the mechanism of action of the drug used to treat that disease are similar in adults and pediatric subpopulations. Clearly, developmental changes in PK and PD need to be considered to enhance the quality of PK-PD modeling and, therefore, increase the success of extrapolation. This article focuses on the influence of differences in PD between adults and pediatric subpopulations that are highly relevant for the use of extrapolation.

An era of single-cell genomics consortia

The human body consists of 37 trillion single cells represented by over 50 organs that are stitched together to make us who we are, yet we still have very little understanding about the basic units of our body: what cell types and states make up our organs both compositionally and spatially. Previous efforts to profile a wide range of human cell types have been attempted by the FANTOM and GTEx consortia. Now, with the advancement in genomic technologies, profiling the human body at single-cell resolution is possible and will generate an unprecedented wealth of data that will accelerate basic and clinical research with tangible applications to future medicine. To date, several major organs have been profiled, but the challenges lie in ways to integrate single-cell genomics data in a meaningful way. In recent years, several consortia have begun to introduce harmonization and equity in data collection and analysis. Herein, we introduce existing and nascent single-cell genomics consortia, and present benefits to necessitate single-cell genomic consortia in a regional environment to achieve the universal human cell reference dataset.

Single-cell transcriptomics in cancer: computational challenges and opportunities

Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer.

Design Approaches for Generating Organ Constructs

Organ constructs are organ-like structures grown in vitro or in vivo that harbor the components, architecture, and function of in vivo organs, in part or in toto. The convergence of stem cell biology, bioengineering, and gene editing tools have substantially broadened our ability to generate various types of organ constructs for regenerative medicine as well as to address pressing biomedical questions. In this Review, we highlight prevailing approaches for generating organ constructs, from organoids to chimeric organ engineering. We also discuss design principles of different approaches, their utility and limitations, and propose strategies to resolve existing hurdles.

Critical Roles of Translation Initiation and RNA Uridylation in Endogenous Retroviral Expression and Neural Differentiation in Pluripotent Stem Cells

Previous studies have suggested that the loss of the translation initiation factor eIF4G1 homolog NAT1 induces excessive self-renewability of naive pluripotent stem cells (PSCs); yet the role of NAT1 in the self-renewal and differentiation of primed PSCs is still unclear. Here, we generate a conditional knockout of NAT1 in primed PSCs and use the cells for the functional analyses of NAT1. Our results show that NAT1 is required for the self-renewal and neural differentiation of primed PSCs. In contrast, NAT1 deficiency in naive pluripotency attenuates the differentiation to all cell types. We also find that NAT1 is involved in efficient protein expression of an RNA uridyltransferase, TUT7. TUT7 is involved in the neural differentiation of primed PSCs via the regulation of human endogenous retrovirus accumulation. These data demonstrate the essential roles of NAT1 and TUT7 in the precise transition of stem cell fate.

Neonatal Organ and Tissue Donation for Research: Options Following Death by Natural Causes

The donation of organs and tissues from neonates (birth to 28 days) for transplantation has been a relatively infrequent occurrence. Less common has been the use of neonatal organs and tissues for research. Specific ethical and legal questions beg for rational and transparent guidelines with which to evaluate referrals of potential donors. Donation of organs and tissues from a neonate can play a key role in the care and support provided to families by health care professionals around the time of a neonate's death. We report on the recovery of neonatal organs and tissues for research. A working group made up of bioethicists, neonatologists, lawyers, obstetric practioners as well as organ procurement and tissue banking professionals evaluated legal, ethical and medical issues. Neonatal donor family members were also consulted. Our primary goals were (a) to ensure that referrals were made in compliance with all applicable federal and state laws, regulations and institutional protocols, and (b) to follow acceptable ethical standards. Algorithms and policies designed to assist in the evaluation of potential neonatal donors were developed. Neonatal donation is proving increasingly valuable for research into areas including diabetes, pulmonary, gastrointestinal, genitourinary and neurological development, rheumatoid arthritis, autism, childhood psychiatric and neurologic disorders, treatment of MRSA infection and pediatric emergency resuscitation. The development of policies and procedures will assist medical professionals who wish to offer the option of donation to family members anticipating the death of a neonate.

Cell Atlas technologies and insights into tissue architecture

Since Robert Hooke first described the existence of 'cells' in 1665, scientists have sought to identify and further characterise these fundamental units of life. While our understanding of cell location, morphology and function has expanded greatly; our understanding of cell types and states at the molecular level, and how these function within tissue architecture, is still limited. A greater understanding of our cells could revolutionise basic biology and medicine. Atlasing initiatives like the Human Cell Atlas aim to identify all cell types at the molecular level, including their physical locations, and to make this reference data openly available to the scientific community. This is made possible by a recent technology revolution: both in single-cell molecular profiling, particularly single-cell RNA sequencing, and in spatially resolved methods for assessing gene and protein expression. Here, we review available and upcoming atlasing technologies, the biological insights gained to date and the promise of this field for the future.

The SARS-CoV-2 receptor ACE2 expression of maternal-fetal interface and fetal organs by single-cell transcriptome study

The new type of pneumonia caused by the SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) has been declared as a global public health concern by WHO. As of April 3, 2020, more than 1,000,000 human infections have been diagnosed around the world, which exhibited apparent person-to-person transmission characteristics of this virus. The capacity of vertical transmission in SARS-CoV-2 remains controversial recently. Angiotensin-converting enzyme 2 (ACE2) is now confirmed as the receptor of SARS-CoV-2 and plays essential roles in human infection and transmission. In present study, we collected the online available single-cell RNA sequencing (scRNA-seq) data to evaluate the cell specific expression of ACE2 in maternal-fetal interface as well as in multiple fetal organs. Our results revealed that ACE2 was highly expressed in maternal-fetal interface cells including stromal cells and perivascular cells of decidua, and cytotrophoblast and syncytiotrophoblast in placenta. Meanwhile, ACE2 was also expressed in specific cell types of human fetal heart, liver and lung, but not in kidney. And in a study containing series fetal and post-natal mouse lung, we observed ACE2 was dynamically changed over the time, and ACE2 was extremely high in neonatal mice at post-natal day 1~3. In summary, this study revealed that the SARS-CoV-2 receptor was widely spread in specific cell types of maternal-fetal interface and fetal organs. And thus, both the vertical transmission and the placenta dysfunction/abortion caused by SARS-CoV-2 need to be further carefully investigated in clinical practice.

Lung regeneration: a tale of mice and men

The respiratory system is the main site of gas exchange with the external environment in complex terrestrial animals. Within the trachea and lungs are multiple different tissue niches each consisting of a myriad of cells types with critical roles in air conduction, gas exchange, providing important niche specific cell-cell interactions, connection to the cardiovascular system, and immune surveillance. How the respiratory system responds to external insults and executes the appropriate regenerative response remains challenging to study given the plethora of cell and tissue interactions for this to occur properly. This review will examine the various cell types and tissue niches found within the respiratory system and provide a comparison between mouse and human lungs and trachea to highlight important similarities and differences. Defining the critical gaps in knowledge in human lung and tracheal regeneration is critical for future development of therapies directed towards respiratory diseases.

Mapping human cell phenotypes to genotypes with single-cell genomics

The cumulative activity of all of the body's cells, with their myriad interactions, life histories, and environmental experiences, gives rise to a condition that is distinctly human and specific to each individual. It is an enduring goal to catalog our human cell types, to understand how they develop, how they vary between individuals, and how they fail in disease. Single-cell genomics has revolutionized this endeavor because sequencing-based methods provide a means to quantitatively annotate cell states on the basis of high-information content and high-throughput measurements. Together with advances in stem cell biology and gene editing, we are in the midst of a fascinating journey to understand the cellular phenotypes that compose human bodies and how the human genome is used to build and maintain each cell. Here, we will review recent advances into how single-cell genomics is being used to develop personalized phenotyping strategies that cross subcellular, cellular, and tissue scales to link our genome to our cumulative cellular phenotypes.

scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles

Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms.

Recent advances in our understanding of the mechanisms of lung alveolarization and bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is the most common cause of morbidity and mortality in preterm infants. A key histopathological feature of BPD is stunted late lung development, where the process of alveolarization-the generation of alveolar gas exchange units-is impeded, through mechanisms that remain largely unclear. As such, there is interest in the clarification both of the pathomechanisms at play in affected lungs, and the mechanisms of de novo alveoli generation in healthy, developing lungs. A better understanding of normal and pathological alveolarization might reveal opportunities for improved medical management of affected infants. Furthermore, disturbances to the alveolar architecture are a key histopathological feature of several adult chronic lung diseases, including emphysema and fibrosis, and it is envisaged that knowledge about the mechanisms of alveologenesis might facilitate regeneration of healthy lung parenchyma in affected patients. To this end, recent efforts have interrogated clinical data, developed new-and refined existing-in vivo and in vitro models of BPD, have applied new microscopic and radiographic approaches, and have developed advanced cell-culture approaches, including organoid generation. Advances have also been made in the development of other methodologies, including single-cell analysis, metabolomics, lipidomics, and proteomics, as well as the generation and use of complex mouse genetics tools. The objective of this review is to present advances made in our understanding of the mechanisms of lung alveolarization and BPD over the period 1 January 2017-30 June 2019, a period that spans the 50th anniversary of the original clinical description of BPD in preterm infants.

The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease

Lung disease accounts for every sixth death globally. Profiling the molecular state of all lung cell types in health and disease is currently revolutionizing the identification of disease mechanisms and will aid the design of novel diagnostic and personalized therapeutic regimens. Recent progress in high-throughput techniques for single-cell genomic and transcriptomic analyses has opened up new possibilities to study individual cells within a tissue, classify these into cell types, and characterize variations in their molecular profiles as a function of genetics, environment, cell-cell interactions, developmental processes, aging, or disease. Integration of these cell state definitions with spatial information allows the in-depth molecular description of cellular neighborhoods and tissue microenvironments, including the tissue resident structural and immune cells, the tissue matrix, and the microbiome. The Human Cell Atlas consortium aims to characterize all cells in the healthy human body and has prioritized lung tissue as one of the flagship projects. Here, we present the rationale, the approach, and the expected impact of a Human Lung Cell Atlas.