A high-resolution map of human RNA translation.

Translated small open reading frames (smORFs) can have important regulatory roles and encode microproteins, yet their genome-wide identification has been challenging. We determined the ribosome locations across six primary human cell types and five tissues and detected 7,767 smORFs with translational profiles matching those of known proteins. The human genome was found to contain highly cell-type- and tissue-specific smORFs and a subset that encodes highly conserved amino acid sequences. Changes in the translational efficiency of upstream-encoded smORFs (uORFs) and the corresponding main ORFs predominantly occur in the same direction. Integration with 456 mass-spectrometry datasets confirms the presence of 603 small peptides at the protein level in humans and provides insights into the subcellular localization of these small proteins. This study provides a comprehensive atlas of high-confidence translated smORFs derived from primary human cells and tissues in order to provide a more complete understanding of the translated human genome.

Transient naive reprogramming corrects hiPS cells functionally and epigenetically.

Cells undergo a major epigenome reconfiguration when reprogrammed to human induced pluripotent stem cells (hiPS cells). However, the epigenomes of hiPS cells and human embryonic stem (hES) cells differ significantly, which affects hiPS cell function1-8. These differences include epigenetic memory and aberrations that emerge during reprogramming, for which the mechanisms remain unknown. Here we characterized the persistence and emergence of these epigenetic differences by performing genome-wide DNA methylation profiling throughout primed and naive reprogramming of human somatic cells to hiPS cells. We found that reprogramming-induced epigenetic aberrations emerge midway through primed reprogramming, whereas DNA demethylation begins early in naive reprogramming. Using this knowledge, we developed a transient-naive-treatment (TNT) reprogramming strategy that emulates the embryonic epigenetic reset. We show that the epigenetic memory in hiPS cells is concentrated in cell of origin-dependent repressive chromatin marked by H3K9me3, lamin-B1 and aberrant CpH methylation. TNT reprogramming reconfigures these domains to a hES cell-like state and does not disrupt genomic imprinting. Using an isogenic system, we demonstrate that TNT reprogramming can correct the transposable element overexpression and differential gene expression seen in conventional hiPS cells, and that TNT-reprogrammed hiPS and hES cells show similar differentiation efficiencies. Moreover, TNT reprogramming enhances the differentiation of hiPS cells derived from multiple cell types. Thus, TNT reprogramming corrects epigenetic memory and aberrations, producing hiPS cells that are molecularly and functionally more similar to hES cells than conventional hiPS cells. We foresee TNT reprogramming becoming a new standard for biomedical and therapeutic applications and providing a novel system for studying epigenetic memory.

Modelling human blastocysts by reprogramming fibroblasts into iBlastoids.

Human pluripotent and trophoblast stem cells have been essential alternatives to blastocysts for understanding early human development1-4. However, these simple culture systems lack the complexity to adequately model the spatiotemporal cellular and molecular dynamics that occur during early embryonic development. Here we describe the reprogramming of fibroblasts into in vitro three-dimensional models of the human blastocyst, termed iBlastoids. Characterization of iBlastoids shows that they model the overall architecture of blastocysts, presenting an inner cell mass-like structure, with epiblast- and primitive endoderm-like cells, a blastocoel-like cavity and a trophectoderm-like outer layer of cells. Single-cell transcriptomics further confirmed the presence of epiblast-, primitive endoderm-, and trophectoderm-like cells. Moreover, iBlastoids can give rise to pluripotent and trophoblast stem cells and are capable of modelling, in vitro, several aspects of the early stage of implantation. In summary, we have developed a scalable and tractable system to model human blastocyst biology; we envision that this will facilitate the study of early human development and the effects of gene mutations and toxins during early embryogenesis, as well as aiding in the development of new therapies associated with in vitro fertilization.

scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

Reprogramming roadmap reveals route to human induced trophoblast stem cells.

The reprogramming of human somatic cells to primed or naive induced pluripotent stem cells recapitulates the stages of early embryonic development1-6. The molecular mechanism that underpins these reprogramming processes remains largely unexplored, which impedes our understanding and limits rational improvements to reprogramming protocols. Here, to address these issues, we reconstruct molecular reprogramming trajectories of human dermal fibroblasts using single-cell transcriptomics. This revealed that reprogramming into primed and naive pluripotency follows diverging and distinct trajectories. Moreover, genome-wide analyses of accessible chromatin showed key changes in the regulatory elements of core pluripotency genes, and orchestrated global changes in chromatin accessibility over time. Integrated analysis of these datasets revealed a role for transcription factors associated with the trophectoderm lineage, and the existence of a subpopulation of cells that enter a trophectoderm-like state during reprogramming. Furthermore, this trophectoderm-like state could be captured, which enabled the derivation of induced trophoblast stem cells. Induced trophoblast stem cells are molecularly and functionally similar to trophoblast stem cells derived from human blastocysts or first-trimester placentas7. Our results provide a high-resolution roadmap for the transcription-factor-mediated reprogramming of human somatic cells, indicate a role for the trophectoderm-lineage-specific regulatory program during this process, and facilitate the direct reprogramming of somatic cells into induced trophoblast stem cells.

Functional annotation of human long noncoding RNAs via molecular phenotyping.

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

ShinyCell: simple and sharable visualization of single-cell gene expression data.

MOTIVATION: As the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. RESULTS: In order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customized in order to maximize their usability and can be easily uploaded to online platforms to facilitate wider access to published data. AVAILABILITY AND IMPLEMENTATION: ShinyCell is available at https://github.com/SGDDNB/ShinyCell and https://figshare.com/projects/ShinyCell/100439. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GeneSwitches: ordering gene expression and functional events in single-cell experiments.

SUMMARY: Emerging single-cell RNA-sequencing data technologies has made it possible to capture and assess the gene expression of individual cells. Based on the similarity of gene expression profiles, many tools have been developed to generate an in silico ordering of cells in the form of pseudo-time trajectories. However, these tools do not provide a means to find the ordering of critical gene expression changes over pseudo-time. We present GeneSwitches, a tool that takes any single-cell pseudo-time trajectory and determines the precise order of gene expression and functional-event changes over time. GeneSwitches uses a statistical framework based on logistic regression to identify the order in which genes are either switched on or off along pseudo-time. With this information, users can identify the order in which surface markers appear, investigate how functional ontologies are gained or lost over time and compare the ordering of switching genes from two related pseudo-temporal processes. AVAILABILITY: GeneSwitches is available at https://geneswitches.ddnetbio.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modelling Water: A Lifetime Enigma.

The first attempt to describe water dates back to 1933 with the Bernal-Fowler model and it would take another forty years before the first computer simulation of liquid water by Barker and Watts in 1969. Since then, over a hundred different water models have been proposed. Despite being widely studied, water remains poorly understood. Examining the evolution of water models, we identified three distinct philosophies in water modelling, namely the employment of effective point charges in pioneering empirical models, the incorporation of polarization to describe many-body inductive effects and the extensive use of ab initio calculations to describe short-range effects. In doing so, we can appraise the current understanding of water and identify attributes that a water model should possess to capture the intricate interactions between water molecules.

Transcriptomic imputation of genetic risk variants uncovers novel whole-blood biomarkers of Parkinson's disease.

Blood-based gene expression signatures could potentially be used as biomarkers for PD. However, it is unclear whether genetically-regulated transcriptomic signatures can provide novel gene candidates for use as PD biomarkers. We leveraged on the Genotype-Tissue Expression (GTEx) database to impute whole-blood transcriptomic expression using summary statistics of three large-scale PD GWAS. A random forest classifier was used with the consensus whole-blood imputed gene signature (IGS) to discriminate between cases and controls. Outcome measures included Area under the Curve (AUC) of Receiver Operating Characteristic (ROC) Curve. We demonstrated that the IGS (n = 37 genes) is conserved across PD GWAS studies and brain tissues. IGS discriminated between cases and controls in an independent whole-blood RNA-sequencing study (1176 PD, 254 prodromal, and 860 healthy controls) with mean AUC and accuracy of 64.8% and 69.4% for PD cohort, and 78.8% and 74% for prodromal cohort. PATL2 was the top-performing imputed gene in both PD and prodromal PD cohorts, whose classifier performance varied with biological sex (higher performance for males and females in the PD and prodromal PD, respectively). Single-cell RNA-sequencing studies (scRNA-seq) of healthy humans and PD patients found PATL2 to be enriched in terminal effector CD8+ and cytotoxic CD4+ cells, whose proportions are both increased in PD patients. We demonstrated the utility of GWAS transcriptomic imputation in identifying novel whole-blood transcriptomic signatures which could be leveraged upon for PD biomarker derivation. We identified PATL2 as a potential biomarker in both clinical and prodromic PD.

GATA4 downregulation enhances CCL20-mediated immunosuppression in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a deadly cancer with a high global mortality rate, and the downregulation of GATA binding protein 4 (GATA4) has been implicated in HCC progression. In this study, we investigated the role of GATA4 in shaping the immune landscape of HCC. METHODS: HCC tumor samples were classified into "low" or "normal/high" based on GATA4 RNA expression relative to adjacent non-tumor liver tissues. The immune landscapes of GATA4-low and GATA4-normal/high tumors were analyzed using cytometry by time-of-flight, bulk/spatial transcriptomic analyses and validated by multiplex immunofluorescence. RESULTS: GATA4-low tumors displayed enrichment in exhausted programmed cell death protein 1+ T cells, immunosuppressive regulatory T cells, myeloid-derived suppressor cells, and macrophages, highlighting the impact of GATA4 downregulation on immunosuppression. Spatial and bulk transcriptomic analyses revealed a negative correlation between GATA4 and C-C Motif Chemokine Ligand 20 (CCL20) expression in HCC. Overexpressing GATA4 confirmed CCL20 as a downstream target, contributing to an immunosuppressive tumor microenvironment, as evidenced by increased regulatory T cells and myeloid-derived suppressor cells in CCL20-high tumors. Lastly, the reduced expression of GATA4 and higher expression of CCL20 were associated with poorer overall survival in patients with HCC, implicating their roles in tumor progression. CONCLUSIONS: Our study reveals that GATA4 downregulation contributes to an immunosuppressive microenvironment, driven by CCL20-mediated enrichment of regulatory T cells and myeloid-derived suppressor cells in HCC. These findings underscore the critical role of GATA4 reduction in promoting immunosuppression and HCC progression.

Deep learning models will shape the future of stem cell research.

Our ability to understand and control stem cell biology is being augmented by developments on two fronts, our ability to collect more data describing cell state and our capability to comprehend these data using deep learning models. Here we consider the impact deep learning will have in the future of stem cell research. We explore the importance of generating data suitable for these methods, the requirement for close collaboration between experimental and computational researchers, and the challenges we face to do this fairly and effectively. Achieving this will ensure that the resulting deep learning models are biologically meaningful and computationally tractable.

Systems level identification of a matrisome-associated macrophage polarisation state in multi-organ fibrosis.

Tissue fibrosis affects multiple organs and involves a master-regulatory role of macrophages which respond to an initial inflammatory insult common in all forms of fibrosis. The recently unravelled multi-organ heterogeneity of macrophages in healthy and fibrotic human disease suggests that macrophages expressing osteopontin (SPP1) associate with lung and liver fibrosis. However, the conservation of this SPP1+ macrophage population across different tissues and its specificity to fibrotic diseases with different etiologies remain unclear. Integrating 15 single-cell RNA-sequencing datasets to profile 235,930 tissue macrophages from healthy and fibrotic heart, lung, liver, kidney, skin, and endometrium, we extended the association of SPP1+ macrophages with fibrosis to all these tissues. We also identified a subpopulation expressing matrisome-associated genes (e.g., matrix metalloproteinases and their tissue inhibitors), functionally enriched for ECM remodelling and cell metabolism, representative of a matrisome-associated macrophage (MAM) polarisation state within SPP1+ macrophages. Importantly, the MAM polarisation state follows a differentiation trajectory from SPP1+ macrophages and is associated with a core set of regulon activity. SPP1+ macrophages without the MAM polarisation state (SPP1+MAM-) show a positive association with ageing lung in mice and humans. These results suggest an advanced and conserved polarisation state of SPP1+ macrophages in fibrotic tissues resulting from prolonged inflammatory cues within each tissue microenvironment.

Single cell analysis in head and neck cancer reveals potential immune evasion mechanisms during early metastasis.

Profiling tumors at single-cell resolution provides an opportunity to understand complexities underpinning lymph-node metastases in head and neck squamous-cell carcinoma. Single-cell RNAseq (scRNAseq) analysis of cancer-cell trajectories identifies a subpopulation of pre-metastatic cells, driven by actionable pathways including AXL and AURK. Blocking these two proteins blunts tumor invasion in patient-derived cultures. Furthermore, scRNAseq analyses of tumor-infiltrating CD8 + T-lymphocytes show two distinct trajectories to T-cell dysfunction, corroborated by their clonal architecture based on single-cell T-cell receptor sequencing. By determining key modulators of these trajectories, followed by validation using external datasets and functional experiments, we uncover a role for SOX4 in mediating T-cell exhaustion. Finally, interactome analyses between pre-metastatic tumor cells and CD8 + T-lymphocytes uncover a putative role for the Midkine pathway in immune-modulation and this is confirmed by scRNAseq of tumors from humanized mice. Aside from specific findings, this study demonstrates the importance of tumor heterogeneity analyses in identifying key vulnerabilities during early metastasis.

Challenges for Computational Stem Cell Biology: A Discussion for the Field.

The first meetup for Computational Stem Cell Biologists was held at the 2020 annual meeting of the International Society for Stem Cell Research. The discussions highlighted opportunities and barriers to computational stem cell research that require coordinated action across the stem cell sector.

Evaluating Capture Sequence Performance for Single-Cell CRISPR Activation Experiments.

The combination of single-cell RNA sequencing with CRISPR inhibition/activation provides a high-throughput approach to simultaneously study the effects of hundreds if not thousands of gene perturbations in a single experiment. One recent development in CRISPR-based single-cell techniques introduces a feature barcoding technology that allows for the simultaneous capture of mRNA and guide RNA (gRNA) from the same cell. This is achieved by introducing a capture sequence, whose complement can be incorporated into each gRNA and that can be used to amplify these features prior to sequencing. However, because the technology is in its infancy, there is little information available on how such experimental parameters can be optimized. To overcome this, we varied the capture sequence, capture sequence position, and gRNA backbone to identify an optimal gRNA scaffold for CRISPR activation gene perturbation studies. We provide a report on our screening approach along with our observations and recommendations for future use.

Transcriptional signature in microglia associated with Aß plaque phagocytosis.

The role of microglia cells in Alzheimer's disease (AD) is well recognized, however their molecular and functional diversity remain unclear. Here, we isolated amyloid plaque-containing (using labelling with methoxy-XO4, XO4+) and non-containing (XO4-) microglia from an AD mouse model. Transcriptomics analysis identified different transcriptional trajectories in ageing and AD mice. XO4+ microglial transcriptomes demonstrated dysregulated expression of genes associated with late onset AD. We further showed that the transcriptional program associated with XO4+ microglia from mice is present in a subset of human microglia isolated from brains of individuals with AD. XO4- microglia displayed transcriptional signatures associated with accelerated ageing and contained more intracellular post-synaptic material than XO4+ microglia, despite reduced active synaptosome phagocytosis. We identified HIF1α as potentially regulating synaptosome phagocytosis in vitro using primary human microglia, and BV2 mouse microglial cells. Together, these findings provide insight into molecular mechanisms underpinning the functional diversity of microglia in AD.

The MURAL collection of prostate cancer patient-derived xenografts enables discovery through preclinical models of uro-oncology.

Preclinical testing is a crucial step in evaluating cancer therapeutics. We aimed to establish a significant resource of patient-derived xenografts (PDXs) of prostate cancer for rapid and systematic evaluation of candidate therapies. The PDX collection comprises 59 tumors collected from 30 patients between 2012-2020, coinciding with availability of abiraterone and enzalutamide. The PDXs represent the clinico-pathological and genomic spectrum of prostate cancer, from treatment-naïve primary tumors to castration-resistant metastases. Inter- and intra-tumor heterogeneity in adenocarcinoma and neuroendocrine phenotypes is evident from bulk and single-cell RNA sequencing data. Organoids can be cultured from PDXs, providing further capabilities for preclinical studies. Using a 1 x 1 x 1 design, we rapidly identify tumors with exceptional responses to combination treatments. To govern the distribution of PDXs, we formed the Melbourne Urological Research Alliance (MURAL). This PDX collection is a substantial resource, expanding the capacity to test and prioritize effective treatments for prospective clinical trials in prostate cancer.

EpiMogrify Models H3K4me3 Data to Identify Signaling Molecules that Improve Cell Fate Control and Maintenance.

The need to derive and culture diverse cell or tissue types in vitro has prompted investigations on how changes in culture conditions affect cell states. However, the identification of the optimal conditions (e.g., signaling molecules and growth factors) required to maintain cell types or convert between cell types remains a time-consuming task. Here, we developed EpiMogrify, an approach that leverages data from ∼100 human cell/tissue types available from ENCODE and Roadmap Epigenomics consortia to predict signaling molecules and factors that can either maintain cell identity or enhance directed differentiation (or cell conversion). EpiMogrify integrates protein-protein interaction network information with a model of the cell's epigenetic landscape based on H3K4me3 histone modifications. Using EpiMogrify-predicted factors for maintenance conditions, we were able to better potentiate the maintenance of astrocytes and cardiomyocytes in vitro. We report a significant increase in the efficiency of astrocyte and cardiomyocyte differentiation using EpiMogrify-predicted factors for conversion conditions.

Correction to: Molecular Interaction Networks to Select Factors for Cell Conversion.

This chapter was published without including the "Conflict of Interest" section given by the author along with the corrected proof.