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Publications in Bioinformatics by NOMIS researchers

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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