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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.

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., 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.

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. Keywords: uPAR; PLAUR; single-cell RNA sequencing; GBM cell invasiveness; CD44; FN1; ECM degradation; inflammatory microenvironment, 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.

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., 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.

Background: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.

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.

Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.

Activation of interferon genes constitutes an important anticancer pathway able to restrict proliferation of cancer cells. Here, we demonstrate that the H3K9me3 histone methyltransferase (HMT) suppressor of variegation 3-9 homolog 1 (SUV39H1) is required for the proliferation of acute myeloid leukemia (AML) and find that its loss leads to activation of the interferon pathway. Mechanistically, we show that this occurs via destabilization of a complex composed of SUV39H1 and the two H3K9me2 HMTs, G9A and GLP. Indeed, loss of H3K9me2 correlated with the activation of key interferon pathway genes, and interference with the activities of G9A/GLP largely phenocopied loss of SUV39H1. Last, we demonstrate that inhibition of G9A/GLP synergized with DNA demethylating agents and that SUV39H1 constitutes a potential biomarker for the response to hypomethylation treatment. Collectively, we uncovered a clinically relevant role for H3K9me2 in safeguarding cancer cells against activation of the interferon pathway.

Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard—SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants.

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.