BloodSpot 3.0: a database of gene and protein expression data in normal and malignant haematopoiesis.

BloodSpot is a specialised database integrating gene expression data from acute myeloid leukaemia (AML) patients related to blood cell development and maturation. The database and interface has helped numerous researchers and clinicians to quickly get an overview of gene expression patterns in healthy and malignant haematopoiesis. Here, we present an update to our framework that includes protein expression data of sorted single cells. With this update we also introduce datasets broadly spanning age groups, which many users have requested, with particular interest for researchers studying paediatric leukaemias. The backend of the database has been rewritten and migrated to a cloud-based environment to accommodate the growth, and provide a better user-experience for our many international users. Users can now enjoy faster transfer speeds and a more responsive interface. In conclusion, the continuing popularity of the database and emergence of new data modalities has prompted us to rewrite and futureproof the back-end, including paediatric centric views, as well as single cell protein data, allowing us to keep the database updated and relevant for the years to come. The database is freely available at www.bloodspot.eu.

The NFIA-ETO2 fusion blocks erythroid maturation and induces pure erythroid leukemia in cooperation with mutant TP53.

The NFIA-ETO2 fusion is the product of a t(1;16)(p31;q24) chromosomal translocation, so far, exclusively found in pediatric patients with pure erythroid leukemia (PEL). To address the role for the pathogenesis of the disease, we facilitated the expression of the NFIA-ETO2 fusion in murine erythroblasts (EBs). We observed that NFIA-ETO2 significantly increased proliferation and impaired erythroid differentiation of murine erythroleukemia cells and of primary fetal liver-derived EBs. However, NFIA-ETO2-expressing EBs acquired neither aberrant in vitro clonogenic activity nor disease-inducing potential upon transplantation into irradiated syngenic mice. In contrast, in the presence of 1 of the most prevalent erythroleukemia-associated mutations, TP53R248Q, expression of NFIA-ETO2 resulted in aberrant clonogenic activity and induced a fully penetrant transplantable PEL-like disease in mice. Molecular studies support that NFIA-ETO2 interferes with erythroid differentiation by preferentially binding and repressing erythroid genes that contain NFI binding sites and/or are decorated by ETO2, resulting in a activity shift from GATA- to ETS-motif-containing target genes. In contrast, TP53R248Q does not affect erythroid differentiation but provides self-renewal and survival potential, mostly via downregulation of known TP53 targets. Collectively, our work indicates that NFIA-ETO2 initiates PEL by suppressing gene expression programs of terminal erythroid differentiation and cooperates with TP53 mutation to induce erythroleukemia.

uPAR (PLAUR) Marks Two Intra-Tumoral Subtypes of Glioblastoma: Insights from Single-Cell RNA Sequencing.

Urokinase plasminogen activator receptor (uPAR) encoded by the PLAUR gene is known as a clinical marker for cell invasiveness in glioblastoma multiforme (GBM). It is additionally implicated in various processes, including angiogenesis and inflammation within the tumor microenvironment. However, there has not been a comprehensive study that depicts the overall functions and molecular cooperators of PLAUR with respect to intra-tumoral subtypes of GBM. Using single-cell RNA sequencing data from 37 GBM patients, we identified PLAUR as a marker gene for two distinct subtypes in GBM. One subtype is featured by inflammatory activities and the other subtype is marked by ECM remodeling processes. Using the whole-transcriptome data from single cells, we are able to uncover the molecular cooperators of PLAUR for both subtypes without presuming biological pathways. Two protein networks comprise the molecular context of PLAUR, with each of the two subtypes characterized by a different dominant network. We concluded that targeting PLAUR directly influences the mechanisms represented by these two protein networks, regardless of the subtype of the targeted cell.

Host RNA Expression Signatures in Young Infants with Urinary Tract Infection: A Prospective Study.

Early diagnosis of infections in young infants remains a clinical challenge. Young infants are particularly vulnerable to infection, and it is often difficult to clinically distinguish between bacterial and viral infections. Urinary tract infection (UTI) is the most common bacterial infection in young infants, and the incidence of associated bacteremia has decreased in the recent decades. Host RNA expression signatures have shown great promise for distinguishing bacterial from viral infections in young infants. This prospective study included 121 young infants admitted to four pediatric emergency care departments in the capital region of Denmark due to symptoms of infection. We collected whole blood samples and performed differential gene expression analysis. Further, we tested the classification performance of a two-gene host RNA expression signature approaching clinical implementation. Several genes were differentially expressed between young infants with UTI without bacteremia and viral infection. However, limited immunological response was detected in UTI without bacteremia compared to a more pronounced response in viral infection. The performance of the two-gene signature was limited, especially in cases of UTI without bloodstream involvement. Our results indicate a need for further investigation and consideration of UTI in young infants before implementing host RNA expression signatures in clinical practice.

Clinical implication of genetic intratumor heterogeneity for targeted therapy in head and neck cancer.

BACKGROUND: Genomic profiling is increasingly used both in therapeutic decision-making and as inclusion criteria for trials testing targeted therapies. However, the mutational landscape may vary across different areas of a tumor and intratumor heterogeneity will challenge treatments or clinical decisions based on single tumor biopsies. The purpose of this study was to assess the clinical relevance of genetic intratumor heterogeneity in head and neck squamous cell carcinomas (HNSCC) using the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT). MATERIALS AND METHODS: This prospective study included 33 whole tumor specimens from 28 patients with primary or recurrent HNSCC referred for surgery. Three tumor blocks were selected from central, semi-peripheral, and peripheral positions, mimicking biopsies in three different locations. Genetic analysis of somatic copy number alterations (SCNAs) was performed on the three biopsies using Oncoscan, focusing on 45 preselected HNSCC genes of interest. Clinical relevance was assessed using the ESCAT score to investigate whether and how treatment decisions would change based on the three biopsies from the same tumor. RESULTS: The SCNAs identified among 45 preselected genes within the three tumor biopsies derived from the same tumor revealed distinct variations. The detected discrepancies could potentially influence treatment approaches or clinical decisions in 36% of the patients if only one tumor biopsy was used. Recurrent tumors exhibited significantly higher variation in SCNAs than primary tumors (p = .024). No significant correlation between tumor size and heterogeneity (p = .7) was observed. CONCLUSION: In 36% of patients diagnosed with HNSCC, clinically significant intratumor heterogeneity was observed which may have implications for patient management. This finding substantiates the need for future studies that specifically investigate the clinical implications associated with intratumor heterogeneity.

Unveiling mRNP composition by fluorescence correlation and cross-correlation spectroscopy using cell lysates.

Understanding the mRNA life cycle requires information about the dynamics and macromolecular composition and stoichiometry of mRNPs. Fluorescence correlation and cross-correlation spectroscopy (FCS and FCCS) are appealing technologies to study these macromolecular structures because they have single molecule sensitivity and readily provide information about their molecular composition and dynamics. Here, we demonstrate how FCS can be exploited to study cytoplasmic mRNPs with high accuracy and reproducibility in cell lysates. Cellular lysates not only recapitulate data from live cells but provide improved readings and allow investigation of single mRNP analysis under particular conditions or following enzymatic treatments. Moreover, FCCS employing minute amounts of cells closely corroborated previously reported RNA dependent interactions and provided estimates of the relative overlap between factors in the mRNPs, thus depicting their heterogeneity. The described lysate-based FCS and FCCS analysis may not only complement current biochemical approaches but also provide novel opportunities for the quantitative analysis of the molecular composition and dynamics of single mRNPs.

ERG promotes the maintenance of hematopoietic stem cells by restricting their differentiation.

The balance between self-renewal and differentiation is crucial for the maintenance of hematopoietic stem cells (HSCs). Whereas numerous gene regulatory factors have been shown to control HSC self-renewal or drive their differentiation, we have relatively few insights into transcription factors that serve to restrict HSC differentiation. In the present work, we identify ETS (E-twenty-six)-related gene (ERG) as a critical factor protecting HSCs from differentiation. Specifically, loss of Erg accelerates HSC differentiation by >20-fold, thus leading to rapid depletion of immunophenotypic and functional HSCs. Molecularly, we could demonstrate that ERG, in addition to promoting the expression of HSC self-renewal genes, also represses a group of MYC targets, thereby explaining why Erg loss closely mimics Myc overexpression. Consistently, the BET domain inhibitor CPI-203, known to repress Myc expression, confers a partial phenotypic rescue. In summary, ERG plays a critical role in coordinating the balance between self-renewal and differentiation of HSCs.

A Protocol for Low-Input RNA-Sequencing of Patients with Febrile Neutropenia Captures Relevant Immunological Information.

Improved methods are needed for diagnosing infectious diseases in children with cancer. Most children have fever for other reasons than bacterial infection and are exposed to unnecessary antibiotics and hospital admission. Recent research has shown that host whole blood RNA transcriptomic signatures can distinguish bacterial infection from other causes of fever. Implementation of this method in clinics could change the diagnostic approach for children with cancer and suspected infection. However, extracting sufficient mRNA to perform transcriptome profiling by standard methods is challenging due to the patient's low white blood cell (WBC) counts. In this prospective cohort study, we succeeded in sequencing 95% of samples from children with leukaemia and suspected infection by using a low-input protocol. This could be a solution to the issue of obtaining sufficient RNA for sequencing from patients with low white blood cell counts. Further studies are required to determine whether the captured immune gene signatures are clinically valid and thus useful to clinicians as a diagnostic tool for patients with cancer and suspected infection.

Benchmarking full-length transcript single cell mRNA sequencing protocols.

BACKGROUND: Single cell mRNA sequencing technologies have transformed our understanding of cellular heterogeneity and identity. For sensitive discovery or clinical marker estimation where high transcript capture per cell is needed only plate-based techniques currently offer sufficient resolution. RESULTS: Here, we present a performance evaluation of four different plate-based scRNA-seq protocols. Our evaluation is aimed towards applications taxing high gene detection sensitivity, reproducibility between samples, and minimum hands-on time, as is required, for example, in clinical use. We included two commercial kits, NEBNext® Single Cell/ Low Input RNA Library Prep Kit (NEB®), SMART-seq® HT kit (Takara®), and the non-commercial protocols Genome & Transcriptome sequencing (G&T) and SMART-seq3 (SS3). G&T delivered the highest detection of genes per single cell. SS3 presented the highest gene detection per single cell at the lowest price. Takara® kit presented similar high gene detection per single cell, and high reproducibility between samples, but at the absolute highest price. NEB® delivered a lower detection of genes but remains an alternative to more expensive commercial kits. CONCLUSION: For the tested kits we found that ease-of-use came at higher prices. Takara can be selected for its ease-of-use to analyse a few samples, but we recommend the cheaper G&T-seq or SS3 for laboratories where a substantial sample flow can be expected.

Human erythroleukemia genetics and transcriptomes identify master transcription factors as functional disease drivers.

Acute erythroleukemia (AEL or acute myeloid leukemia [AML]-M6) is a rare but aggressive hematologic malignancy. Previous studies showed that AEL leukemic cells often carry complex karyotypes and mutations in known AML-associated oncogenes. To better define the underlying molecular mechanisms driving the erythroid phenotype, we studied a series of 33 AEL samples representing 3 genetic AEL subgroups including TP53-mutated, epigenetic regulator-mutated (eg, DNMT3A, TET2, or IDH2), and undefined cases with low mutational burden. We established an erythroid vs myeloid transcriptome-based space in which, independently of the molecular subgroup, the majority of the AEL samples exhibited a unique mapping different from both non-M6 AML and myelodysplastic syndrome samples. Notably, >25% of AEL patients, including in the genetically undefined subgroup, showed aberrant expression of key transcriptional regulators, including SKI, ERG, and ETO2. Ectopic expression of these factors in murine erythroid progenitors blocked in vitro erythroid differentiation and led to immortalization associated with decreased chromatin accessibility at GATA1-binding sites and functional interference with GATA1 activity. In vivo models showed development of lethal erythroid, mixed erythroid/myeloid, or other malignancies depending on the cell population in which AEL-associated alterations were expressed. Collectively, our data indicate that AEL is a molecularly heterogeneous disease with an erythroid identity that results in part from the aberrant activity of key erythroid transcription factors in hematopoietic stem or progenitor cells.

BloodSpot: a database of healthy and malignant haematopoiesis updated with purified and single cell mRNA sequencing profiles.

BloodSpot is a gene-centric database of mRNA expression of haematopoietic cells. The web-based interface to the database includes three concomitant levels of visualization for a gene query; foremost is the expression across hematopoietic cell types, second is analysis of survival of Acute Myeloid Leukaemia patients based on gene expression, and lastly, the expression visualized in an interactive developmental tree. With the introduction of single cell data we have now also included an unbiased dimensionality reduction method to show gene expression over the continuum of haematopoiesis. The webserver includes a few select analysis functionalities, like Student's t-test, identification of correlating genes and lookup of whole genetic signatures, with the aim of making generation and testing of hypotheses quick and intuitive. The visualizations have been updated to accommodate new datatypes and the database has been largely expanded with RNA-sequencing datasets, both purified in bulk and at single cell resolution, increasing the number of single samples more than 10 fold, while keeping simplicity in presentation. The database should be of interest for any researcher within leukaemia, haematopoiesis, cellular development, or stem cells. The database is freely available at www.bloodspot.eu.

An atlas of active enhancers across human cell types and tissues.

Enhancers control the correct temporal and cell-type-specific activation of gene expression in multicellular eukaryotes. Knowing their properties, regulatory activity and targets is crucial to understand the regulation of differentiation and homeostasis. Here we use the FANTOM5 panel of samples, covering the majority of human tissues and cell types, to produce an atlas of active, in vivo-transcribed enhancers. We show that enhancers share properties with CpG-poor messenger RNA promoters but produce bidirectional, exosome-sensitive, relatively short unspliced RNAs, the generation of which is strongly related to enhancer activity. The atlas is used to compare regulatory programs between different cells at unprecedented depth, to identify disease-associated regulatory single nucleotide polymorphisms, and to classify cell-type-specific and ubiquitous enhancers. We further explore the utility of enhancer redundancy, which explains gene expression strength rather than expression patterns. The online FANTOM5 enhancer atlas represents a unique resource for studies on cell-type-specific enhancers and gene regulation.

Single Cell Sequencing in Cancer Diagnostics.

Personalized medicine has been driven by improvements in genomic sequencing and analysis. For several diseases, in particular cancers, it has for nearly a decade been standard clinical practice to analyze the genome and expression of the genes of patients. The results are reflected directly in the treatment plan for the patient, in targeted medical inventions. This specialized mode of diagnostics has been restricted to account for averaged trends in the tumor. The approach sharply contrasts our knowledge on heterogeneity within tumors. Several studies further describe how treatment against one tumor subclone in some cases merely serves to provide space and support for uncontrolled growth of more aggressive subclones. In this chapter, we describe current possibilities for implementation of single cell sequencing of malignomas in clinic, as well as discuss hands-on practical advice for single cell routine diagnostics that allows for full delineation of tumor clonality.

Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data.

BACKGROUND: Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural networks, namely autoencoders, has been useful for denoising of single cell data, imputation of missing values and dimensionality reduction. RESULTS: Here, we present a striking feature with the potential to greatly increase the usability of autoencoders: With specialized training, the autoencoder is not only able to generalize over the data, but also to tease apart biologically meaningful modules, which we found encoded in the representation layer of the network. Our model can, from scRNA-seq data, delineate biological meaningful modules that govern a dataset, as well as give information as to which modules are active in each single cell. Importantly, most of these modules can be explained by known biological functions, as provided by the Hallmark gene sets. CONCLUSIONS: We discover that tailored training of an autoencoder makes it possible to deconvolute biological modules inherent in the data, without any assumptions. By comparisons with gene signatures of canonical pathways we see that the modules are directly interpretable. The scope of this discovery has important implications, as it makes it possible to outline the drivers behind a given effect of a cell. In comparison with other dimensionality reduction methods, or supervised models for classification, our approach has the benefit of both handling well the zero-inflated nature of scRNA-seq, and validating that the model captures relevant information, by establishing a link between input and decoded data. In perspective, our model in combination with clustering methods is able to provide information about which subtype a given single cell belongs to, as well as which biological functions determine that membership.

Deep sequencing of human papillomavirus positive loco-regionally advanced oropharyngeal squamous cell carcinomas reveals novel mutational signature.

BACKGROUND: The genetic profile for human papilloma virus positive (HPV+) oropharyngeal squamous cell carcinomas (OPSCC) remains largely unknown. The purpose of this study was to sequence tissue material from a large cohort of locoregionally-advanced HPV+ OPSCCs. METHODS: We performed targeted deep sequencing of 395 cancer-associated genes in 114 matched tumor/normal loco-regionally advanced HPV+ OPSCCs. Mutations and copy number aberrations were determined. RESULTS: We identified a total of 3459 mutations with an average of 10 mutations per megabase and a median of 28 variants per sample. The most frequently mutated genes were KALRN (28%), SPTBN1 (32%), KMT2A (31%), ZNRF3 (9%), BNC2 (12%), NOTCH2 (25%), FGFR2 (12%), SMAD2 (6%), and AR (13%). Our findings were dominated by COSMIC signature 5 and 12, represented in other head and neck cancers and in hepatocellular carcinomas, respectively. CONCLUSIONS: We have identified multiple genetic aberrations in HPV+ OPSCCs, and the COSMIC signature 12 as most prevalent. The mutations harbour both therapeutic and prognostic potential.

BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis.

Research on human and murine haematopoiesis has resulted in a vast number of gene-expression data sets that can potentially answer questions regarding normal and aberrant blood formation. To researchers and clinicians with limited bioinformatics experience, these data have remained available, yet largely inaccessible. Current databases provide information about gene-expression but fail to answer key questions regarding co-regulation, genetic programs or effect on patient survival. To address these shortcomings, we present BloodSpot (www.bloodspot.eu), which includes and greatly extends our previously released database HemaExplorer, a database of gene expression profiles from FACS sorted healthy and malignant haematopoietic cells. A revised interactive interface simultaneously provides a plot of gene expression along with a Kaplan-Meier analysis and a hierarchical tree depicting the relationship between different cell types in the database. The database now includes 23 high-quality curated data sets relevant to normal and malignant blood formation and, in addition, we have assembled and built a unique integrated data set, BloodPool. Bloodpool contains more than 2000 samples assembled from six independent studies on acute myeloid leukemia. Furthermore, we have devised a robust sample integration procedure that allows for sensitive comparison of user-supplied patient samples in a well-defined haematopoietic cellular space.

shRNA screening identifies JMJD1C as being required for leukemia maintenance.

Epigenetic regulatory mechanisms are implicated in the pathogenesis of acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL). Recent progress suggests that proteins involved in epigenetic control are amenable to drug intervention, but little is known about the cancer-specific dependency on epigenetic regulators for cell survival and proliferation. We used a mouse model of human AML induced by the MLL-AF9 fusion oncogene and an epigenetic short hairpin RNA (shRNA) library to screen for novel potential drug targets. As a counter-screen for general toxicity of shRNAs, we used normal mouse bone marrow cells. One of the best candidate drug targets identified in these screens was Jmjd1c. Depletion of Jmjd1c impairs growth and colony formation of mouse MLL-AF9 cells in vitro as well as establishment of leukemia after transplantation. Depletion of JMJD1C impairs expansion and colony formation of human leukemic cell lines, with the strongest effect observed in the MLL-rearranged ALL cell line SEM. In both mouse and human leukemic cells, the growth defect upon JMJD1C depletion appears to be primarily due to increased apoptosis, which implicates JMJD1C as a potential therapeutic target in leukemia.

Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients.

Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional programs that differ between cancer and corresponding normal cells and has not been efficient in identifying expression changes fundamental to disease etiology. Here we present a method that facilitates the comparison of any cancer sample to its nearest normal cellular counterpart, using acute myeloid leukemia (AML) as a model. We first generated a gene expression-based landscape of the normal hematopoietic hierarchy, using expression profiles from normal stem/progenitor cells, and next mapped the AML patient samples to this landscape. This allowed us to identify the closest normal counterpart of individual AML samples and determine gene expression changes between cancer and normal. We find the cancer vs normal method (CvN method) to be superior to conventional methods in stratifying AML patients with aberrant karyotype and in identifying common aberrant transcriptional programs with potential importance for AML etiology. Moreover, the CvN method uncovered a novel poor-outcome subtype of normal-karyotype AML, which allowed for the generation of a highly prognostic survival signature. Collectively, our CvN method holds great potential as a tool for the analysis of gene expression profiles of cancer patients.

HemaExplorer: a database of mRNA expression profiles in normal and malignant haematopoiesis.

The HemaExplorer (http://servers.binf.ku.dk/hemaexplorer) is a curated database of processed mRNA Gene expression profiles (GEPs) that provides an easy display of gene expression in haematopoietic cells. HemaExplorer contains GEPs derived from mouse/human haematopoietic stem and progenitor cells as well as from more differentiated cell types. Moreover, data from distinct subtypes of human acute myeloid leukemia is included in the database allowing researchers to directly compare gene expression of leukemic cells with those of their closest normal counterpart. Normalization and batch correction lead to full integrity of the data in the database. The HemaExplorer has comprehensive visualization interface that can make it useful as a daily tool for biologists and cancer researchers to assess the expression patterns of genes encountered in research or literature. HemaExplorer is relevant for all research within the fields of leukemia, immunology, cell differentiation and the biology of the haematopoietic system.

Whole genome sequencing in clinical practice.

Whole genome sequencing (WGS) is becoming the preferred method for molecular genetic diagnosis of rare and unknown diseases and for identification of actionable cancer drivers. Compared to other molecular genetic methods, WGS captures most genomic variation and eliminates the need for sequential genetic testing. Whereas, the laboratory requirements are similar to conventional molecular genetics, the amount of data is large and WGS requires a comprehensive computational and storage infrastructure in order to facilitate data processing within a clinically relevant timeframe. The output of a single WGS analyses is roughly 5 MIO variants and data interpretation involves specialized staff collaborating with the clinical specialists in order to provide standard of care reports. Although the field is continuously refining the standards for variant classification, there are still unresolved issues associated with the clinical application. The review provides an overview of WGS in clinical practice - describing the technology and current applications as well as challenges connected with data processing, interpretation and clinical reporting.