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Large language models (LLMs) have greatly enhanced our ability to understand biology and chemistry. Yet, relatively few robust methods have been reported for structure-based drug discovery. Highly precise biomolecule-ligand interaction datasets are urgently needed in particular for LLMs, that require extensive training data. We present MISATO, the first dataset that combines quantum mechanics properties of small molecules and associated molecular dynamics simulations of about 20000 experimental protein-ligand complexes. Starting from the PDBbind dataset, semi-empirical quantum mechanics was used to systematically refine these structures. The largest collection to date of molecular dynamics traces of protein-ligand complexes in explicit water are included, accumulating to 170 μs. We give ML baseline models and simple Python data loaders, and aim to foster a thriving community around MISATO (https://github.com/t7morgen/misato-dataset). An easy entry point for ML experts is provided without the need of deep domain expertise to enable the next generation of drug discovery AI models.

BackgroundMany automatic approaches to brain tumor segmentation employ multiple magnetic resonance imaging (MRI) sequences. The goal of this project was to compare different combinations of input sequences to determine which MRI sequences are needed for effective automated brain metastasis (BM) segmentation.MethodsWe analyzed preoperative imaging (T1-weighted sequence ± contrast-enhancement (T1/T1-CE), T2-weighted sequence (T2), and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence) from 333 patients with BMs from six centers. A baseline 3D U-Net with all four sequences and six U-Nets with plausible sequence combinations (T1-CE, T1, T2-FLAIR, T1-CE+T2-FLAIR, T1-CE+T1+T2-FLAIR, T1-CE+T1) were trained on 239 patients from two centers and subsequently tested on an external cohort of 94 patients from four centers.ResultsThe model based on T1-CE alone achieved the best segmentation performance for BM segmentation with a median Dice similarity coefficient (DSC) of 0.96. Models trained without T1-CE performed worse (T1-only: DSC = 0.70 and T2-FLAIR-only: DSC = 0.72). For edema segmentation, models that included both T1-CE and T2-FLAIR performed best (DSC = 0.93), while the remaining four models without simultaneous inclusion of these both sequences reached a median DSC of 0.81-0.89.ConclusionsA T1-CE-only protocol suffices for the segmentation of BMs. The combination of T1-CE and T2-FLAIR is important for edema segmentation. Missing either T1-CE or T2-FLAIR decreases performance. These findings may improve imaging routines by omitting unnecessary sequences, thus allowing for faster procedures in daily clinical practice while enabling optimal neural network-based target definitions.

Metabolic rewiring is essential to enable cancer onset and progression. One important metabolic pathway that is often hijacked by cancer cells is the one-carbon cycle, in which the third carbon of serine is oxidized to formate. We have previously shown that formate production in cancer cells often exceeds the anabolic demand, resulting in formate overflow. Furthermore, we observed that high extracellular formate promotes thein vitroinvasiveness of glioblastoma (GBM) cells. However, additional data supporting thisin vitroobservation and mechanistic details remained elusive so far.In the present study, we now demonstrate that inhibition of formate overflow results in a decreased invasiveness of GBM cellsex vivoandin vivo. Additionally, we observed that exposure to exogeneous formate can induce a transiently stable pro-invasive phenotype that results in increased metastasis formationin vivo. All in all, these results suggest that a local formate increase within the tumor microenvironment may be one factor that can promote cancer cell motility and dissemination.Mechanistically, we uncover a previously undescribed interplay where formate acts as a trigger to alter fatty acid metabolism and matrix metalloproteinase (MMP) activity which in turn impacts cancer cell invasiveness. We thus highlight the role of formate as a pro-invasive metabolite. Gaining a deeper understanding of formate overflow and how it promotes invasion in cancer, may open new therapeutic opportunities to prevent cancer cell dissmination.

Interstitial lung disease (ILD) is a heterogenous group of lung disorders where destruction and incomplete regeneration of the lung parenchyma often results in persistent architectural distortion of the pulmonary scaffold. Continuous mesenchyme-centered, disease-relevant signaling likely initiates and perpetuates the fibrotic remodeling process, specifically targeting the epithelial cell compartment, thereby destroying the gas exchange area.With the aim of identifying functionally relevant mediators of the lung mesenchymal-epithelial crosstalk that hold potential as new targets for therapeutic strategies, we developed a 3D organoid co-culture model based on human induced pluripotent stem cell-derived alveolar epithelial type 2 cells that form alveolar organoids in presence of lung fibroblasts from ILD patients as well as a control cell line (IMR-90). While organoid formation capacity and size was comparable in the presence of ILD or control lung fibroblasts, metabolic activity was significantly increased in ILD co-cultures. Alveolar organoids cultured with ILD fibroblasts further demonstrated reduced stem cell function as reflected by reducedSurfactant Protein Cgene expression together with an aberrant basaloid-prone differentiation program indicated by elevatedCadherin 2, Bone Morphogenic Protein 4andVimentintranscription.In order to screen for key mediators of the misguided mesenchymal-to-epithelial crosstalk with a focus on disease-relevant inflammatory processes, we used mass spectrometry and characterized the secretome of end stage ILD lung fibroblasts in comparison to non-chronic lung disease (CLD) patient fibroblasts. Out of the over 2000 proteins detected by this experimental approach, 47 proteins were differentially abundant comparing ILD and non-CLD fibroblast secretome.The ILD secretome profile was dominated by chemokines, includingCXCL1, CXCL3, andCXCL8, interfering with growth factor signaling orchestrated byInterleukin 11 (IL11), steering fibrogenic cell-cell communication, and proteins regulating extracellular matrix remodeling including epithelial-to-mesenchymal transition. When in turn treating alveolar organoids withIL11, we recapitulated the co-culture results obtained with primary ILD fibroblasts including changes in metabolic activity.In summary, we identified mediators likely contributing to the disease-perpetuating mesenchymal-to-epithelial crosstalk in ILD. In our alveolar organoid co-cultures, we were able to highlight the importance of fibroblast-initiated aberrant epithelial differentiation and confirmedIL11as a key player in ILD pathogenesis by unbiased ILD fibroblast secretome analysis.Abstract Figure

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 selections often rely on marker genes, precluding them from detecting continuous spatial signals or novel states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both probe set specificity for cell type identification and within-cell-type expression variation to resolve spatially distinct populations while taking into account prior knowledge, as well as probe design and expression constraints. To facilitate data analysis and interpretation, Spapros also provides rules for cell type identification. We evaluated Spapros by selecting probes on 6 different data sets and built an evaluation pipeline with 12 quality metrics to find that Spapros outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a SCRINSHOT experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types. Spapros enables optimal probe set selection, probe set evaluation, and probe design, as a freely available Python package.

CRISPR interference (CRISPRi), the targeting of a catalytically dead Cas protein to block transcription, is the leading technique to silence gene expression in bacteria. However, design rules for CRISPRi remain poorly defined, limiting predictable design for gene interrogation, pathway manipulation, and high-throughput screens. Here we develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in multiple genome-wide essentiality screens, with the surprising discovery that gene-specific features such as transcriptional activity substantially impact prediction of guide activity. Accounting for these features as part of algorithm development allowed us to develop a mixed-effect random forest regression model that provides better estimates of guide efficiency than existing methods, as demonstrated in an independent saturating screen. We further applied methods from explainable AI to extract interpretable design rules from the model, such as sequence preferences in the vicinity of the PAM distinct from those previously described for genome engineering applications. Our approach provides a blueprint for the development of predictive models for CRISPR technologies where only indirect measurements of guide activity are available.

The advent of medical imaging and automatic image analysis is bringing the full quantitative assessment of lesions and tumor burden at every clinical examination within reach. This opens avenues for the development and testing of functional disease models, as well as their use in the clinical practice for personalized medicine. In this paper, we introduce a Bayesian statistical framework, based on mixed-effects models, to quantitatively test and learn functional disease models at different scales, on population longitudinal data. We also derive an effective mathematical model for the crossover between initially detected lesions and tumor dissemination, based on the Iwata-Kawasaki-Shigesada model. We finally propose to leverage this descriptive disease progression model into model-aware biomarkers for personalized risk-assessment, taking all available examinations and relevant covariates into account. As a use case, we study Multiple Myeloma, a disseminated plasma cell cancer, in which proper diagnostics is essential, to differentiate frequent precursor state without end-organ damage from the rapidly developing disease requiring therapy. After learning the best biological models for local lesion growth and global tumor burden evolution on clinical data, and computing corresponding population priors, we use individual model parameters as biomarkers, and can study them systematically for correlation with external covariates, such as sex or location of the lesion. On our cohort of 63 patients with smoldering Multiple Myeloma, we show that they perform substantially better than other radiological criteria, to predict progression into symptomatic Multiple Myeloma. Our study paves the way for modeling disease progression patterns for Multiple Myeloma, but also for other metastatic and disseminated tumor growth processes, and for analyzing large longitudinal image data sets acquired in oncological imaging. It shows the unprecedented potential of model-based biomarkers for better and more personalized treatment decisions and deserves being validated on larger cohorts to establish its role in clinical decision making.

SUMMARYTissue clearing methods enable imaging of intact biological specimens without sectioning. However, reliable and scalable analysis of such large imaging data in 3D remains a challenge. Towards this goal, we developed a deep learning-based framework to quantify and analyze the brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a fully convolutional network with a transfer learning approach for segmentation. We systematically analyzed vascular features of the whole brains including their length, bifurcation points and radius at the micrometer scale by registering them to the Allen mouse brain atlas. We reported the first evidence of secondary intracranial collateral vascularization in CD1-Elite mice and found reduced vascularization in the brainstem as compared to the cerebrum. VesSAP thus enables unbiased and scalable quantifications for the angioarchitecture of the cleared intact mouse brain and yields new biological insights related to the vascular brain function.GRAPHICAL ABSTRACTSupporting material of VesSAP is available athttp://DISCOtechnologies.org/VesSAP