Lukas Heumos

Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH) | HZM · Institute of Computational Biology

Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most inform...

Motivation Pangenome graphs offer a comprehensive way of capturing genomic variability across multiple genomes. However, current construction methods often introduce biases, excluding complex sequences or relying on references. The PanGenome Graph Builder (PGGB) addresses these issues. To date, though, there is no state-of-the-art pipeline allowing...

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exp...

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental, or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely st...

Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. H...

Motivation Pangenome graphs offer a comprehensive way of capturing genomic variability across multiple genomes. However, current construction methods often introduce biases, excluding complex sequences or relying on references. The PanGenome Graph Builder (PGGB) addresses these issues. To date, though, there is no state-of-the-art pipeline allowing...

Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at kn...

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here, we introduce ehrapy, a modular open-source Python framework designed for ex...

Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) wi...

Mass spectrometry has become an indispensable tool in the life sciences. The new major version 3 of the computational framework OpenMS provides significant advancements regarding open, scalable, and reproducible high-throughput workflows for proteomics, metabolomics, and oligonucleotide mass spectrometry. OpenMS makes analyses from emerging fields...

Single cell multiplexing techniques (cell hashing and genetic multiplexing) allow to combine multiple samples, thereby optimizing sample processing and reducing batch effects. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggre...

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integ...

Motivation: Machine learning has shown extensive growth in recent years and is now routinely applied to sensitive areas. To allow appropriate verification of predictive models before deployment, models must be deterministic. Solely fixing all random seeds is not sufficient for deterministic machine learning, as major machine learning libraries def...

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of...

Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) wi...

Targeted spatial transcriptomics methods capture the topology of cell types and states in tissues at single cell- and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing and interpreting the spatial signals present in a tissue. However, current sel...

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with r...

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with r...

nbproject is an open-source Python tool to help manage Jupyter notebooks with metadata, dependency, and integrity tracking. A draft-to-publish workflow creates more reproducible notebooks with context. There are a number of approaches to address reproducibility & manageability problems of computational R&D projects. nbproject complements - and shou...

Metachromatic leukodystrophy (MLD) is a rare genetic disorder caused by mutations in the Arylsulfatase-A (ARSA) gene. The enzyme plays a key role in sulfatide metabolism in brain cells, and its deficiency leads to neurodegeneration. The clinical manifestations of MLD include stagnation and decline of motor and cognitive function, leading to prematu...

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community. View-only version: https://rdcu.be/cv5H7

Single-cell RNA-seq datasets are often first analyzed independently without harnessing model fits from previous studies, and are then contextualized with public data sets, requiring time-consuming data wrangling. We address these issues with sfaira, a single-cell data zoo for public data sets paired with a model zoo for executable pre-trained model...

Machine learning has shown extensive growth in recent years. However, previously existing studies highlighted a reproducibility crisis in machine learning. The reasons for irreproducibility are manifold. Major machine learning libraries default to the usage of non-deterministic algorithms based on atomic operations. Solely fixing all random seeds i...

Mutations in the human β-globin gene are the cause of β-hemoglobinopathies, one of the most common inherited single-gene blood disorders in the world. Novel therapeutic approaches are based on lentiviral vectors (LVs) or CRISPR-Cas9-mediated gene disruption to express adult hemoglobin (HbA), or to reactivate the completely functional fetal hemoglob...

β-hemoglobinopathies are caused by abnormal or absent production of hemoglobin in the blood due to mutations in the β-globin gene (HBB). Imbalanced expression of adult hemoglobin (HbA) induces strong anemia in patients suffering from the disease. However, individuals with natural-occurring mutations in the HBB cluster or related genes, compensate t...

The pandemicity & the ability of the SARS-COV-2 to reinfect a cured subject, among other damaging characteristics of it, took everybody by surprise. A global collaborative scientific effort was direly required to bring learned people from different niches of medicine & data science together. Such a platform was provided by COVID19 Virtual BioHackat...

As part of the virtual BioHackathon 2020, we formed a working group that focused on the analysis of gene expression in the context of COVID-19. More specifically, we performed transcriptome analyses on published datasets in order to better understand the interaction between the human host and the SARS-CoV-2 virus.The ideas proposed during this hack...

Personalized multi-peptide vaccines are currently discussed intensively for tumor immunotherapy. In order to identify epitopes - short, immunogenic peptides - suitable for eliciting a tumor-specific immune response, human leukocyte antigen (HLA)-presented peptides are isolated by immunoaffinity purification from cancer tissue samples and analyzed b...