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Publications in Bioinformatics by NOMIS researchers

Comprehensive interrogation of synthetic lethality in the DNA damage response

NOMIS Researcher(s)

Published in

April 9, 2025

The DNA damage response (DDR) is a multifaceted network of pathways that preserves genome stability1,2. Unravelling the complementary interplay between these pathways remains a challenge3,4. Here we used CRISPR interference (CRISPRi) screening to comprehensively map the genetic interactions required for survival during normal human cell homeostasis across all core DDR genes. We captured known interactions and discovered myriad new connections that are available online. We defined the molecular mechanism of two of the strongest interactions. First, we found that WDR48 works with USP1 to restrain PCNA degradation in FEN1/LIG1-deficient cells. Second, we found that SMARCAL1 and FANCM directly unwind TA-rich DNA cruciforms, preventing catastrophic chromosome breakage by the ERCC1–ERCC4 complex. Our data yield fundamental insights into genome maintenance, provide a springboard for mechanistic investigations into new connections between DDR factors and pinpoint synthetic vulnerabilities that could be exploited in cancer therapy.

Partnership(s)
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Research field(s)
Bioinformatics, Biochemistry & Molecular Biology, Genetics & Heredity, Oncology & Carcinogenesis

DOI
10.1038/s41586-025-08815-4

AI-based approach to dissect the variability of mouse stem cell-derived embryo models

NOMIS Researcher(s)

Published in

February 19, 2025

Recent advances in stem cell-derived embryo models have transformed developmental biology, offering insights into embryogenesis without the constraints of natural embryos. However, variability in their development challenges research standardization. To address this, we use deep learning to enhance the reproducibility of selecting stem cell-derived embryo models. Through live imaging and AI-based models, we classify 900 mouse post-implantation stem cell-derived embryo-like structures (ETiX-embryos) into normal and abnormal categories. Our best-performing model achieves 88% accuracy at 90 h post-cell seeding and 65% accuracy at the initial cell-seeding stage, forecasting developmental trajectories. Our analysis reveals that normally developed ETiX-embryos have higher cell counts and distinct morphological features such as larger size and more compact shape. Perturbation experiments increasing initial cell numbers further supported this finding by improving normal development outcomes. This study demonstrates deep learning’s utility in improving embryo model selection and reveals critical features of ETiX-embryo self-organization, advancing consistency in this evolving field.

Research field(s)
Bioinformatics, Artificial Intelligence & Image Processing, Biophysics, Developmental Biology

DOI
10.1038/s41467-025-56908-5

Resolving the three-dimensional interactome of human accelerated regions during human and chimpanzee neurodevelopment

NOMIS Researcher(s)

Published in

January 30, 2025

Human accelerated regions (HARs) have been implicated in human brain evolution. However, insight into the genes and pathways they control is lacking, hindering the understanding of their function. Here, we identify 2,963 conserved gene targets for 1,590 HARs and their orthologs in human and chimpanzee neural stem cells (NSCs). Conserved gene targets are enriched for neurodevelopmental functions and are overrepresented among differentially expressed genes (DEGs) identified in human NSCs (hNSCs) and chimpanzee NSCs (cNSCs) as well as in human versus non-human primate brains. Species-specific gene targets do not converge on any function and are not enriched among DEGs. HAR targets also show cell-type-specific expression in the human fetal brain, including in outer radial glia, which are linked to cortical expansion. Our findings support that HARs influence brain evolution by altering the expression of ancestral gene targets shared between human and chimpanzee rather than by gaining new targets in human and facilitate hypothesis-directed studies of HAR biology.

Research field(s)
Bioinformatics, Developmental Biology, Evolutionary Biology

DOI
10.1016/j.cell.2025.01.007

Spatial transcriptomic clocks reveal cell proximity effects in brain ageing

NOMIS Researcher(s)

Published in

December 18, 2024

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain ageing is complex and is accompanied by many cellular changes2. Furthermore, the influence that aged cells have on neighbouring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in ageing tissues have not yet been developed. Here we generate a spatially resolved single-cell transcriptomics brain atlas of 4.2 million cells from 20 distinct ages across the adult lifespan and across two rejuvenating interventions—exercise and partial reprogramming. We build spatial ageing clocks, machine learning models trained on this spatial transcriptomics atlas, to identify spatial and cell-type-specific transcriptomic fingerprints of ageing, rejuvenation and disease, including for rare cell types. Using spatial ageing clocks and deep learning, we find that T cells, which increasingly infiltrate the brain with age, have a marked pro-ageing proximity effect on neighbouring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating proximity effect on neighbouring cells. We also identify potential mediators of the pro-ageing effect of T cells and the pro-rejuvenating effect of neural stem cells on their neighbours. These results suggest that rare cell types can have a potent influence on their neighbours and could be targeted to counter tissue ageing. Spatial ageing clocks represent a useful tool for studying cell–cell interactions in spatial contexts and should allow scalable assessment of the efficacy of interventions for ageing and disease.

Research field(s)
Bioinformatics, Biochemistry & Molecular Biology, Immunology, Neurology & Neurosurgery

DOI
10.1038/s41586-024-08334-8

Whole-mouse clearing and imaging at the cellular level with vDISCO

NOMIS Researcher(s)

Published in

January 25, 2023

Homeostatic and pathological phenomena often affect multiple organs across the whole organism. Tissue clearing methods, together with recent advances in microscopy, have made holistic examinations of biological samples feasible. Here, we report the detailed protocol for nanobody(VHH)-boosted 3D imaging of solvent-cleared organs (vDISCO), a pressure-driven, nanobody-based whole-body immunolabeling and clearing method that renders whole mice transparent in 3 weeks, consistently enhancing the signal of fluorescent proteins, stabilizing them for years. This allows the reliable detection and quantification of fluorescent signal in intact rodents enabling the analysis of an entire body at cellular resolution. Here, we show the high versatility of vDISCO applied to boost the fluorescence signal of genetically expressed reporters and clear multiple dissected organs and tissues, as well as how to image processed samples using multiple fluorescence microscopy systems. The entire protocol is accessible to laboratories with limited expertise in tissue clearing. In addition to its applications in obtaining a whole-mouse neuronal projection map, detecting single-cell metastases in whole mice and identifying previously undescribed anatomical structures, we further show the visualization of the entire mouse lymphatic system, the application for virus tracing and the visualization of all pericytes in the brain. Taken together, our vDISCO pipeline allows systematic and comprehensive studies of cellular phenomena and connectivity in whole bodies. © 2023, Springer Nature Limited.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Bioinformatics

DOI
10.1038/s41596-022-00788-2

Characterizing super-spreaders using population-level weighted social networks in rural communities

NOMIS Researcher(s)

Published in

January 1, 2022

Sociocentric network maps of entire populations, when combined with data on the nature of constituent dyadic relationships, offer the dual promise of advancing understanding of the relevance of networks for disease transmission and of improving epidemic forecasts. Here, using detailed sociocentric data collected over 4 years in a population of 24 702 people in 176 villages in Honduras, along with diarrhoeal and respiratory disease prevalence, we create a social-network-powered transmission model and identify super-spreading nodes as well as the nodes most vulnerable to infection, using agent-based Monte Carlo network simulations. We predict the extent of outbreaks for communicable diseases based on detailed social interaction patterns. Evidence from three waves of population-level surveys of diarrhoeal and respiratory illness indicates a meaningful positive correlation with the computed super-spreading capability and relative vulnerability of individual nodes. Previous research has identified super-spreaders through retrospective contact tracing or simulated networks. By contrast, our simulations predict that a node’s super-spreading capability and its vulnerability in real communities are significantly affected by their connections, the nature of the interaction across these connections, individual characteristics (e.g. age and sex) that affect a person’s ability to disperse a pathogen, and also the intrinsic characteristics of the pathogen (e.g. infectious period and latency). This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Bioinformatics

DOI
10.1098/rsta.2021.0123

PnB Designer: a web application to design prime and base editor guide RNAs for animals and plants

NOMIS Researcher(s)

Published in

December 1, 2021

Background: The rapid expansion of the CRISPR toolbox through tagging effector domains to either enzymatically inactive Cas9 (dCas9) or Cas9 nickase (nCas9) has led to several promising new gene editing strategies. Recent additions include CRISPR cytosine or adenine base editors (CBEs and ABEs) and the CRISPR prime editors (PEs), in which a deaminase or reverse transcriptase are fused to nCas9, respectively. These tools hold great promise to model and correct disease-causing mutations in animal and plant models. But so far, no widely-available tools exist to automate the design of both BE and PE reagents. Results: We developed PnB Designer, a web-based application for the design of pegRNAs for PEs and guide RNAs for BEs. PnB Designer makes it easy to design targeting guide RNAs for single or multiple targets on a variant or reference genome from organisms spanning multiple kingdoms. With PnB Designer, we designed pegRNAs to model all known disease causing mutations available in ClinVar. Additionally, PnB Designer can be used to design guide RNAs to install or revert a SNV, scanning the genome with one CBE and seven different ABE PAM variants and returning the best BE to use. PnB Designer is publicly accessible at http://fgcz-shiny.uzh.ch/PnBDesigner/ Conclusion: With PnB Designer we created a user-friendly design tool for CRISPR PE and BE reagents, which should simplify choosing editing strategy and avoiding design complications.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Bioinformatics

DOI
10.1186/s12859-021-04034-6

CRISPR off-target detection with DISCOVER-seq

NOMIS Researcher(s)

Published in

May 1, 2020

DISCOVER-seq (discovery of in situ Cas off-targets and verification by sequencing) is a broadly applicable approach for unbiased CRISPR–Cas off-target identification in cells and tissues. It leverages the recruitment of DNA repair factors to double-strand breaks (DSBs) after genome editing with CRISPR nucleases. Here, we describe a detailed experimental protocol and analysis pipeline with which to perform DISCOVER-seq. The principle of this method is to track the precise recruitment of MRE11 to DSBs by chromatin immunoprecipitation followed by next-generation sequencing. A customized open-source bioinformatics pipeline, BLENDER (blunt end finder), then identifies off-target sequences genome wide. DISCOVER-seq is capable of finding and measuring off-targets in primary cells and in situ. The two main advantages of DISCOVER-seq are (i) low false-positive rates because DNA repair enzyme binding is required for genome edits to occur and (ii) its applicability to a wide variety of systems, including patient-derived cells and animal models. The whole protocol, including the analysis, can be completed within 2 weeks.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Bioinformatics

DOI
10.1038/s41596-020-0309-5

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

NOMIS Researcher(s)

Published in

January 1, 2019

Motivation Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Bioinformatics

DOI
10.1093/bioinformatics/bty537

Predicting age from the transcriptome of human dermal fibroblasts

NOMIS Researcher(s)

Published in

December 20, 2018

Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Bioinformatics

DOI
10.1186/s13059-018-1599-6

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