LifeTime and improving European healthcare through cell-based interceptive medicine

CHD8 haploinsufficiency links autism to transient alterations in excitatory and inhibitory trajectories

Mutations in the chromodomain helicase DNA-binding 8 (CHD8) gene are a frequent cause of autism spectrum disorder (ASD). While its phenotypic spectrum often encompasses macrocephaly, implicating cortical abnormalities, how CHD8 haploinsufficiency affects neurodevelopmental is unclear. Here, employing human cerebral organoids, we find that CHD8 haploinsufficiency disrupted neurodevelopmental trajectories with an accelerated and delayed generation of, respectively, inhibitory and excitatory neurons that yields, at days 60 and 120, symmetrically opposite expansions in their proportions. This imbalance is consistent with an enlargement of cerebral organoids as an in vitro correlate of patients' macrocephaly. Through an isogenic design of patient-specific mutations and mosaic organoids, we define genotype-phenotype relationships and uncover their cell-autonomous nature. Our results define cell-type-specific CHD8-dependent molecular defects related to an abnormal program of proliferation and alternative splicing. By identifying cell-type-specific effects of CHD8 mutations, our study uncovers reproducible developmental alterations that may be employed for neurodevelopmental disease modeling.

Towards standardizing retinal optical coherence tomography angiography: a review

The visualization and assessment of retinal microvasculature are important in the study, diagnosis, monitoring, and guidance of treatment of ocular and systemic diseases. With the introduction of optical coherence tomography angiography (OCTA), it has become possible to visualize the retinal microvasculature volumetrically and without a contrast agent. Many lab-based and commercial clinical instruments, imaging protocols and data analysis methods and metrics, have been applied, often inconsistently, resulting in a confusing picture that represents a major barrier to progress in applying OCTA to reduce the burden of disease. Open data and software sharing, and cross-comparison and pooling of data from different studies are rare. These inabilities have impeded building the large databases of annotated OCTA images of healthy and diseased retinas that are necessary to study and define characteristics of specific conditions. This paper addresses the steps needed to standardize OCTA imaging of the human retina to address these limitations. Through review of the OCTA literature, we identify issues and inconsistencies and propose minimum standards for imaging protocols, data analysis methods, metrics, reporting of findings, and clinical practice and, where this is not possible, we identify areas that require further investigation. We hope that this paper will encourage the unification of imaging protocols in OCTA, promote transparency in the process of data collection, analysis, and reporting, and facilitate increasing the impact of OCTA on retinal healthcare delivery and life science investigations.

Temporal and spatial topography of cell proliferation in cancer

Proliferation is a fundamental trait of cancer cells, but its properties and spatial organization in tumours are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and then develop robust, multivariate, proliferation metrics. Across diverse cancers, proliferative architecture is organized at two spatial scales: large domains, and smaller niches enriched for specific immune lineages. Some tumour cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as 'cell cycle coherence'. By contrast, the cell cycles of other tumour cell populations are skewed towards specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive tumour behaviour. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.

MITI minimum information guidelines for highly multiplexed tissue images

The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.

From cohorts to molecules: Adverse impacts of endocrine disrupting mixtures

Convergent evidence associates exposure to endocrine disrupting chemicals (EDCs) with major human diseases, even at regulation-compliant concentrations. This might be because humans are exposed to EDC mixtures, whereas chemical regulation is based on a risk assessment of individual compounds. Here, we developed a mixture-centered risk assessment strategy that integrates epidemiological and experimental evidence. We identified that exposure to an EDC mixture in early pregnancy is associated with language delay in offspring. At human-relevant concentrations, this mixture disrupted hormone-regulated and disease-relevant regulatory networks in human brain organoids and in the model organisms Xenopus leavis and Danio rerio, as well as behavioral responses. Reinterrogating epidemiological data, we found that up to 54% of the children had prenatal exposures above experimentally derived levels of concern, reaching, for the upper decile compared with the lowest decile of exposure, a 3.3 times higher risk of language delay.

Optimal transport improves cell-cell similarity inference in single-cell omics data

MOTIVATION

High-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity are typically achieved through unsupervised clustering, which crucially relies on a similarity metric.

RESULTS

We here propose the use of Optimal Transport (OT) as a cell-cell similarity metric for single-cell omics data. OT defines distances to compare high-dimensional data represented as probability distributions. To speed up computations and cope with the high dimensionality of single-cell data, we consider the entropic regularization of the classical OT distance. We then extensively benchmark OT against state-of-the-art metrics over 13 independent datasets, including simulated, scRNA-seq, scATAC-seq and single-cell DNA methylation data. First, we test the ability of the metrics to detect the similarity between cells belonging to the same groups (e.g. cell types, cell lines of origin). Then, we apply unsupervised clustering and test the quality of the resulting clusters. OT is found to improve cell-cell similarity inference and cell clustering in all simulated and real scRNA-seq data, as well as in scATAC-seq and single-cell DNA methylation data.

AVAILABILITY AND IMPLEMENTATION

All our analyses are reproducible through the OT-scOmics Jupyter notebook available at https://github.com/ComputationalSystemsBiology/OT-scOmics.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

Toward a granular molecular-anatomic map of the blood vasculature - single-cell RNA sequencing makes the leap

Single-cell RNA sequencing (scRNAseq) marks the birth of a new era in physiology and medicine. Within foreseeable future, we will know exactly what genes are expressed - and at what levels - in all the different cell types and subtypes that make up our bodies. We will also learn how a particular cell state, whether it occurs during development, tissue repair, or disease, reflects precise changes in gene expression. While profoundly impacting all areas of life science, scRNAseq may lead to a particular leap in vascular biology research. Blood vessels pervade and fulfill essential functions in all organs, but the functions differ. Innumerable organ-specific vascular adaptations and specializations are required. These, in turn, are dictated by differential gene expression by the two principal cellular building blocks of blood vessels: endothelial cells and mural cells. An organotypic vasculature is essential for functions as diverse as thinking, gas exchange, urine excretion, and xenobiotic detoxification in the brain, lung, kidney, and liver, respectively. In addition to the organotypicity, vascular cells also differ along the vascular arterio-venous axis, referred to as zonation, differences that are essential for the regulation of blood pressure and flow. Moreover, gene expression-based molecular changes dictate states of cellular activity, necessary for angiogenesis, vascular permeability, and immune cell trafficking, i.e. functions necessary for development, inflammation, and repair. These different levels of cellular heterogeneity create a nearly infinite phenotypic diversity among vascular cells. In this review, I summarize and exemplify what scRNAseq has brought to the picture in just a few years and point out where it will take us.

What sequencing technologies can teach us about innate immunity

For years, we have taken a reductionist approach to understanding gene regulation through the study of one gene in one cell at a time. While this approach has been fruitful it is laborious and fails to provide a global picture of what is occurring in complex situations involving tightly coordinated immune responses. The emergence of whole-genome techniques provides a system-level view of a response and can provide a plethora of information on events occurring in a cell from gene expression changes to splicing changes and chemical modifications. As with any technology, this often results in more questions than answers, but this wealth of knowledge is providing us with an unprecedented view of what occurs inside our cells during an immune response. In this review, we will discuss the current RNA-sequencing technologies and what they are helping us learn about the innate immune system.

Complexity against current cancer research - are we on the wrong track?

Cancer genetics has led to major discoveries, including proto-oncogene and tumor-suppressor concepts, and cancer genomics generated concepts like driver and passenger genes, revealed tumor heterogeneity and clonal evolution. Reconstructing trajectories of tumorigenesis using spatial and single-cell genomics is possible. Patient stratification and prognostic parameters have been improved. Yet, despite these advances, successful translation into targeted therapies has been scarce and mostly limited to kinase inhibitors. Here, we argue that current cancer research may be on the wrong track, by considering cancer more as a "monogenic" disease, trying to extract common information from thousands of patients, while not properly considering complexity and individual diversity. We suggest to empower a systems cancer approach which reconstructs the information network that has been altered by the tumorigenic events, to analyze hierarchies and predict (druggable) key nodes that could interfere with/block the aberrant information transfer. We also argue that the inter-individual variability between patients of similar cohorts is too high to extract common polygenic network information from large numbers of patients and argue in favor of an individualized approach. The analysis we propose would require a structured multinational and multidisciplinary effort, in which clinicians, and cancer, developmental, cell and computational biologists together with mathematicians and informaticians develop dynamic regulatory networks which integrate the entire information transfer in and between cells and organs in (patho)physiological conditions, revealing hierarchies and available drugs to interfere with key regulators. Based on this blueprint, the altered information transfer in individual cancers could be modeled and possible targeted (combo)therapies proposed. This article is protected by copyright. All rights reserved.

From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries

Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.

Mitogen-activated protein kinase activity drives cell trajectories in colorectal cancer

In colorectal cancer, oncogenic mutations transform a hierarchically organized and homeostatic epithelium into invasive cancer tissue lacking visible organization. We sought to define transcriptional states of colorectal cancer cells and signals controlling their development by performing single-cell transcriptome analysis of tumors and matched non-cancerous tissues of twelve colorectal cancer patients. We defined patient-overarching colorectal cancer cell clusters characterized by differential activities of oncogenic signaling pathways such as mitogen-activated protein kinase and oncogenic traits such as replication stress. RNA metabolic labeling and assessment of RNA velocity in patient-derived organoids revealed developmental trajectories of colorectal cancer cells organized along a mitogen-activated protein kinase activity gradient. This was in contrast to normal colon organoid cells developing along graded Wnt activity. Experimental targeting of EGFR-BRAF-MEK in cancer organoids affected signaling and gene expression contingent on predictive KRAS/BRAF mutations and induced cell plasticity overriding default developmental trajectories. Our results highlight directional cancer cell development as a driver of non-genetic cancer cell heterogeneity and re-routing of trajectories as a response to targeted therapy.

The Prognostic Model Based on Tumor Cell Evolution Trajectory Reveals a Different Risk Group of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and heterogeneity of HCC is the major barrier in improving patient outcome. To stratify HCC patients with different degrees of malignancy and provide precise treatment strategies, we reconstructed the tumor evolution trajectory with the help of scRNA-seq data and established a 30-gene prognostic model to identify the malignant state in HCC. Patients were divided into high-risk and low-risk groups. C-index and receiver operating characteristic (ROC) curve confirmed the excellent predictive value of this model. Downstream analysis revealed the underlying molecular and functional characteristics of this model, including significantly higher genomic instability and stronger proliferation/progression potential in the high-risk group. In summary, we established a novel prognostic model to overcome the barriers caused by HCC heterogeneity and provide the possibility of better clinical management for HCC patients to improve their survival outcomes.

Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features

With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration.

Single-cell omics: Overview, analysis, and application in biomedical science

Single-cell sequencing methods provide the highest resolution insight into cellular heterogeneity. Owing to their rapid growth and decreasing cost, they are now widely accessible to scientists worldwide. Single-cell technologies enable analysis of a large number of cells, making them powerful tools to characterise rare cell types and refine our understanding of diverse cell states. Moreover, single-cell application in biomedical sciences helps to unravel mechanisms related to disease pathogenesis and outcome. In this Viewpoint, we briefly describe existing single-cell methods (genomics, transcriptomics, epigenomics, proteomics, and mulitomics), comment on available analysis tools, and give examples of method applications in the biomedical field.

The genetic architecture of primary biliary cholangitis

Primary biliary cholangitis (PBC) is a rare autoimmune disease of the liver affecting the small bile ducts. From a genetic point of view, PBC is a complex trait and several genetic and environmental factors have been called in action to explain its etiopathogenesis. Similarly to other complex traits, PBC has benefited from the introduction of genome-wide association studies (GWAS), which identified many variants predisposing or protecting toward the development of the disease. While a progressive endeavour toward the characterization of candidate loci and downstream pathways is currently ongoing, there is still a relatively large portion of heritability of PBC to be revealed. In addition, genetic variation behind progression of the disease and therapeutic response are mostly to be investigated yet. This review outlines the state-of-the-art regarding the genetic architecture of PBC and provides some hints for future investigations, focusing on the study of gene-gene interactions, the application of whole-genome sequencing techniques, and the investigation of X chromosome that can be helpful to cover the missing heritability gap in PBC.

Single-Cell Analysis for Whole-Organism Datasets

Cell atlases are essential companions to the genome as they elucidate how genes are used in a cell type-specific manner or how the usage of genes changes over the lifetime of an organism. This review explores recent advances in whole-organism single-cell atlases, which enable understanding of cell heterogeneity and tissue and cell fate, both in health and disease. Here we provide an overview of recent efforts to build cell atlases across species and discuss the challenges that the field is currently facing. Moreover, we propose the concept of having a knowledgebase that can scale with the number of experiments and computational approaches and a new feedback loop for development and benchmarking of computational methods that includes contributions from the users. These two aspects are key for community efforts in single-cell biology that will help produce a comprehensive annotated map of cell types and states with unparalleled resolution.

Breast cancer heterogeneity through the lens of single-cell analysis and spatial pathologies

Breast cancer ecosystems are composed of complex cell types, including tumor, stromal and immune cells, each of which can assume diverse phenotypes. Both the heterogeneous composition and spatially distinct tumor microenvironment impact breast cancer progression, treatment response and therapeutic resistance. Thus, a deeper understanding of breast cancer heterogeneity may help facilitate the development of novel therapies and improve outcomes for patients. The advent of paradigm shifting single-cell analysis and spatial pathologies allows for a comprehensive analysis of the tumor ecosystem as well as the interactions between its components at unprecedented resolution. In this review, we discuss the insights gained through single-cell analysis and spatial pathologies on breast cancer heterogeneity.

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing

Next-generation sequencing technologies have revolutionised the study of biological systems by enabling the examination of a broad range of tissues. Its application to single-cell genomics has generated a dynamic and evolving field with a vast amount of research highlighting heterogeneity in transcriptional, genetic and epigenomic state between cells. However, compared to these aspects of cellular heterogeneity, relatively little has been gleaned from single-cell datasets regarding cellular mitochondrial heterogeneity. Single-cell sequencing techniques can provide coverage of the mitochondrial genome which allows researchers to probe heteroplasmies at the level of the single cell, and observe interactions with cellular function. In this review, we give an overview of two popular single-cell modalities-single-cell RNA sequencing and single-cell ATAC sequencing-whose throughput and widespread usage offers researchers the chance to probe heteroplasmy combined with cell state in detailed resolution across thousands of cells. After summarising these technologies in the context of mitochondrial research, we give an overview of recent methods which have used these approaches for discovering mitochondrial heterogeneity. We conclude by highlighting current limitations of these approaches and open problems for future consideration.

Patient-derived tumor models: a more suitable tool for pre-clinical studies in colorectal cancer

Colorectal cancer (CRC), despite the advances in screening and surveillance, remains the second most common cause of cancer death worldwide. The biological inadequacy of pre-clinical models to fully recapitulate the multifactorial etiology and the complexity of tumor microenvironment and human CRC's genetic heterogeneity has limited cancer treatment development. This has led to the development of Patient-derived models able to phenocopy as much as possible the original inter- and intra-tumor heterogeneity of CRC, reflecting the tumor microenvironment's cellular interactions. Implantation of patient tissue into immunodeficient mice hosts and the culture of tumor organoids have allowed advances in cancer biology and metastasis. This review highlights the advantages and limits of Patient-derived models as innovative and valuable pre-clinical tools to study progression and metastasis of CRC, develop novel therapeutic strategies by creating a drug screening platform, and predict the efficacy of clinical response to therapy.

Opportunities and challenges for microRNA-targeting therapeutics for epilepsy

Epilepsy is a common and serious neurological disorder characterised by recurrent spontaneous seizures. Frontline pharmacotherapy includes small-molecule antiseizure drugs that typically target ion channels and neurotransmitter systems, but these fail in 30% of patients and do not prevent either the development or progression of epilepsy. An emerging therapeutic target is microRNA (miRNA), small noncoding RNAs that negatively regulate sets of proteins. Their multitargeting action offers unique advantages for certain forms of epilepsy with complex underlying pathophysiology, such as temporal lobe epilepsy (TLE). miRNA can be inhibited by designed antisense oligonucleotides (ASOs; e.g., antimiRs). Here, we outline the prospects for miRNA-based therapies. We review design considerations for nucleic acid-based approaches and the challenges and next steps in developing therapeutic miRNA-targeting molecules for epilepsy.

Computational principles and challenges in single-cell data integration

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.

Bayesian inference of gene expression states from single-cell RNA-seq data

Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data.

Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.

Artificial intelligence in cancer research: learning at different levels of data granularity

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

COVID-19 and the human innate immune system

The introduction of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into the human population represents a tremendous medical and economic crisis. Innate immunity-as the first line of defense of our immune system-plays a central role in combating this novel virus. Here, we provide a conceptual framework for the interaction of the human innate immune system with SARS-CoV-2 to link the clinical observations with experimental findings that have been made during the first year of the pandemic. We review evidence that variability in innate immune system components among humans is a main contributor to the heterogeneous disease courses observed for coronavirus disease 2019 (COVID-19), the disease spectrum induced by SARS-CoV-2. A better understanding of the pathophysiological mechanisms observed for cells and soluble mediators involved in innate immunity is a prerequisite for the development of diagnostic markers and therapeutic strategies targeting COVID-19. However, this will also require additional studies addressing causality of events, which so far are lagging behind.

Single-Cell Transcriptomics Supports a Role of CHD8 in Autism

Chromodomain helicase domain 8 (CHD8) is one of the most frequently mutated and most penetrant genes in the autism spectrum disorder (ASD). Individuals with CHD8 mutations show leading symptoms of autism, macrocephaly, and facial dysmorphisms. The molecular and cellular mechanisms underpinning the early onset and development of these symptoms are still poorly understood and prevent timely and more efficient therapies of patients. Progress in this area will require an understanding of "when, why and how cells deviate from their normal trajectories". High-throughput single-cell RNA sequencing (sc-RNAseq) directly quantifies information-bearing RNA molecules that enact each cell's biological identity. Here, we discuss recent insights from sc-RNAseq of CRISPR/Cas9-editing of Chd8/CHD8 during mouse neocorticogenesis and human cerebral organoids. Given that the deregulation of the balance between excitation and inhibition (E/I balance) in cortical and subcortical circuits is thought to represent a major etiopathogenetic mechanism in ASD, we focus on the question of whether, and to what degree, results from current sc-RNAseq studies support this hypothesis. Beyond that, we discuss the pros and cons of these approaches and further steps to be taken to harvest the full potential of these transformative techniques.

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

[The Use of Artificial Intelligence in Alzheimer's Disease - Personalized Diagnostics and Therapy]

Using the example of dementia in Alzheimer's disease, it is shown which opportunities but also risks are posed by newer methodological approaches of artificial intelligence (AI) for the diagnosis and treatment of Alzheimer's dementia (AD). In addition, AI is examined in the context of an ethical-philosophical critique of technology.

Translational insights from single-cell technologies across the cardiovascular disease continuum

Cardiovascular disease is the leading cause of death worldwide. The societal health burden it represents can be reduced by taking preventive measures and developing more effective therapies. Reaching these goals, however, requires a better understanding of the pathophysiological processes leading to and occurring in the diseased heart. In the last 5 years, several biological advances applying single-cell technologies have enabled researchers to study cardiovascular diseases with unprecedented resolution. This has produced many new insights into how specific cell types change their gene expression level, activation status and potential cellular interactions with the development of cardiovascular disease, but a comprehensive overview of the clinical implications of these findings is lacking. In this review, we summarize and discuss these recent advances and the promise of single-cell technologies from a translational perspective across the cardiovascular disease continuum, covering both animal and human studies, and explore the future directions of the field.

Platelet Proteomes, Pathways, and Phenotypes as Informants of Vascular Wellness and Disease

Platelets rapidly undergo responsive transitions in form and function to repair vascular endothelium and mediate hemostasis. In contrast, heterogeneous platelet subpopulations with a range of primed or refractory phenotypes gradually arise in chronic inflammatory and other conditions in a manner that may indicate or support disease. Qualitatively distinguishable platelet phenotypes are increasingly associated with a variety of physiological and pathological circumstances; however, the origins and significance of platelet phenotypic variation remain unclear and conceptually vague. As changes in platelet function in disease exhibit many similarities to platelets following the activation of platelet agonist receptors, the intracellular responses of platelets common to hemostasis and inflammation may provide insights to the molecular basis of platelet phenotype. Here, we review concepts around how protein-level relations-from platelet receptors through intracellular signaling events-may help to define platelet phenotypes in inflammation, immune responses, aging, and other conditions. We further discuss how representing systems-wide platelet proteomics data profiles as circuit-like networks of causally related intracellular events, or, pathway maps, may inform molecular definitions of platelet phenotype. In addition to offering insights into platelets as druggable targets, maps of causally arranged intracellular relations underlying platelet function can also advance precision and interceptive medicine efforts by leveraging platelets as accessible, dynamic, endogenous, circulating biomarkers of vascular wellness and disease. Graphic Abstract: A graphic abstract is available for this article.

Regenerative medicine clinical readiness

Regenerative medicine, poised to transform 21st century healthcare, has aspired to enrich care options by bringing cures to patients in need. Science-driven responsible and regulated translation of innovative technology has enabled the launch of previously unimaginable care pathways adopted prudently for select serious diseases and disabilities. The collective resolve to advance the design, manufacture and validity of affordable regenerative solutions aims to democratize such health benefits for all. The objective of this Review is to outline the framework and prerequisites that underpin clinical readiness of regenerative care. Integrated research and development, specialized workforce education and accessible evidence-based practice implementation are at the core of realizing an equitable regenerative medicine vision.

COVID-19 lessons from the dish: Dissecting CNS manifestations through brain organoids

COVID-19 is increasingly understood as a systemic disease with pathogenic manifestations beyond the respiratory tract. Recent work by Ramani et al (2020) dissects the cellular and molecular mechanisms of SARS-CoV-2's neurotrophic properties, using viral exposure of human brain organoids. Their findings highlight neurons as primary target of cerebral SARS-CoV-2 infection and uncover its Tau-related neurotoxicity.

Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma

Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to ten human lung adenocarcinomas and ten normal control tissues. Our analyses revealed heterogeneous carcinoma cell transcriptomes reflecting histological grade and oncogenic pathway activities, and two distinct microenvironmental patterns. The immune-activated CP²E microenvironment was composed of cancer-associated myofibroblasts, proinflammatory monocyte-derived macrophages, plasmacytoid dendritic cells and exhausted CD8+ T cells, and was prognostically unfavorable. In contrast, the inert N³MC microenvironment was characterized by normal-like myofibroblasts, non-inflammatory monocyte-derived macrophages, NK cells, myeloid dendritic cells and conventional T cells, and was associated with a favorable prognosis. Microenvironmental marker genes and signatures identified in single-cell profiles had progonostic value in bulk tumor profiles. In summary, single-cell RNA profiling of lung adenocarcinoma provides additional prognostic information based on the microenvironment, and may help to predict therapy response and to reveal possible target cell populations for future therapeutic approaches.

Stem Cells and Organoid Technology in Precision Medicine in Inflammation: Are We There Yet?

Individualised cellular models of disease are a key tool for precision medicine to recapitulate chronic inflammatory processes. Organoid models can be derived from induced pluripotent stem cells (iPSCs) or from primary stem cells ex vivo. These models have been emerging over the past decade and have been used to reconstruct the respective organ-specific physiology and pathology, at an unsurpassed depth. In cancer research, patient-derived cancer organoids opened new perspectives in predicting therapy response and provided novel insights into tumour biology. In precision medicine of chronic inflammatory disorders, stem-cell based organoid models are currently being evaluated in pre-clinical pharmacodynamic studies (clinical studies in a dish) and are employed in clinical studies, e.g., by re-transplanting autologous epithelial organoids to re-establish intestinal barrier integrity. A particularly exciting feature of iPSC systems is their ability to provide insights into organ systems and inflammatory disease processes, which cannot be monitored with clinical biopsies, such as immune reactions in neurodegenerative disorders. Refinement of differentiation protocols, and next-generation co-culturing methods, aimed at generating self-organised, complex tissues in vitro, will be the next logical steps. In this mini-review, we critically discuss the current state-of-the-art stem cell and organoid technologies, as well as their future impact, potential and promises in combating immune-mediated chronic diseases.

Single-cell analyses of aging, inflammation and senescence

Single-cell gene expression (transcriptomics) data are becoming robust and abundant, and are increasingly used to track organisms along their life-course. This allows investigation into how aging affects cellular transcriptomes, and how changes in transcriptomes may underlie aging, including chronic inflammation (inflammaging), immunosenescence and cellular senescence. We compiled and tabulated aging-related single-cell datasets published to date, collected and discussed relevant findings, and inspected some of these datasets ourselves. We specifically note insights that cannot (or not easily) be based on bulk data. For example, in some datasets, the fraction of cells expressing p16 (CDKN2A), one of the most prominent markers of cellular senescence, was reported to increase, in addition to its upregulated mean expression over all cells. Moreover, we found evidence for inflammatory processes in most datasets, some of these driven by specific cells of the immune system. Further, single-cell data are specifically useful to investigate whether transcriptional heterogeneity (also called noise or variability) increases with age, and many (but not all) studies in our review report an increase in such heterogeneity. Finally, we demonstrate some stability of marker gene expression patterns across closely similar studies and suggest that single-cell experiments may hold the key to provide detailed insights whenever interventions (countering aging, inflammation, senescence, disease, etc.) are affecting cells depending on cell type.

Advances and challenges in single-cell RNA-seq of microbial communities

Microbes have developed complex strategies to respond to their environment and escape the immune system by individualizing their behavior. While single-cell RNA sequencing has become instrumental for studying mammalian cells, its use with fungi, protozoa and bacteria is still in its infancy. In this review, we highlight the major progress towards mapping the molecular states of microbes at the single-cell level using genome-wide transcriptomics and describe how these technologies can be extended to probe thousands of species at high throughput. We envision that mammalian and microbial single-cell profiling could soon be integrated for the study of microbial communities in health and disease.

Automated identification of the mouse brain's spatial compartments from in situ sequencing data

Background

Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments.

Results

Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles.

Conclusion

We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.

Thinking "ethical" when designing an international, cross-disciplinary biomedical research consortium

This commentary outlines challenges with identifying and implementing ethical, legal and societal considerations when initiating large-scale scientific programs and suggests best practices to ensure responsible research.