Biology subjects

Rusch, M.

Publications and source records attributed to Rusch, M..

4 recordsLinked to original sources

bioRxiv2026

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

Alternative lengthening of telomeres (ALT) is a telomerase-independent pathway used by aggressive cancers to maintain their replicative immortality. Because ALT is absent from normal human cells, it is an appealing target for cancer therapy, but the lack of ability to determine ALT status at scale has hindered discovery. Here, we developed ALTitude, a machine learning method from a tabular foundation model that infers ALT from cell line whole genome sequencing data, without need for paired germline analysis. We deployed ALTitude across the DepMap, doubling the number of known ALT+ cancer models. Systematic integration of ALTitude with CRISPR-Cas9 screens yielded the selective dependency on SMARCAL1 in ALT+ cell lines, where we show it stabilizes the ALT phenotype. Acute depletion of SMARCAL1 leads to G2/M arrest, mitotic catastrophe, and cell death. These data provide a valuable resource for studying ALT-related genomic features and present SMARCAL1 as a therapeutic target for ALT+ malignancies.

cancer biology↗

bioRxiv2023

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

systems biology↗

bioRxiv2022

Morphological Subprofile Analysis for Bioactivity Annotation of Small Molecules

Fast prediction of mode of action for bioactive compounds would immensely foster bioactivity annotation in compound collections and may early on reveal off-targets in chemical biology research and drug discovery. A variety of target-based assays is available for addressing the modulation of druggable proteins. However, they cannot precisely predict how a compound would influence cellular processes due to polypharmacology. Furthermore, non-protein targets are often not considered. Morphological profiling, e.g., using the Cell Painting assay that monitors hundreds of morphological features upon compound perturbation and staining of cellular components, offers a fast, unbiased assessment of compound activity on various targets and cellular processes in one single experiment. However, due to incomplete bioactivity annotation and unknown activities of reference (landmark) compounds, prediction of bioactivity is not straightforward. Here we introduce the concept of subprofile analysis to map the mode of action for both reference and unexplored compounds. We defined mode-of-action clusters for a group of reference compounds and extracted cluster subprofiles that contain only a subset of morphological features (i.e., subprofiles) to represent a consensus profile. Subprofile analysis allows for assignment of compounds to, currently, ten different targets or modes of action in one single assay and bypasses the need of exploring all biosimilar reference compounds for the generation of target hypothesis. This approach will enable rapid bioactivity annotation of compound collections, particularly of uncharacterized small molecules, and will be extended to further bioactivity clusters in future. The data is public accessible via https://github.com/mpimp-comas/2022_pahl_ziegler_subprofiles and the web app tool http://cpcse.pythonanywhere.com/.

cell biology↗

bioRxiv2019

RNAIndel: a machine-learning framework for discovery of somatic coding indels using tumor RNA-Seq data

Reliable identification of expressed somatic insertion/deletion (indels) is an unmet demand due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for discovery of somatic indels in tumor trasncriptome.\n\nBy implementing features characterized by PCR-free whole-genome and whole-exome sequencing into a machine-learning framework, we present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data alone. RNAIndel robustly predicts 87{square}93% of somatic indels from 235 samples with heterogeneous conditions, even recovering subclonal (VAF range 0.01-0.15) driver indels missed by targeted deep-sequencing, outperforming the current best-practice for RNA-Seq variant calling which had 57% sensitivity but with 12 times more false positives.\n\nRNAIndel is freely available at https://github.com/stjude/RNAIndel\n\nContactjinghui.zhang@stjude.org

bioinformatics↗