Lasseigne Lab

Lasseigne Lab

Because Science.

The Lasseigne Lab develops and applies genomic strategies to map molecular processes contributing to the etiology, progression, and treatment of diseases originating in the brain and/or the kidney. We investigate the impact of cell- and tissue-specific gene and transcript regulation and expression, cell-cell communication, and sex-associated molecular changes on disease manifestation. Through computational machine learning and data-driven approaches, we identify optimal precision preclinical models and nominate and prioritize drug targets and repositioning candidates for treating patient cellular phenotypes.

Some of the questions our lab asks are: If Mendelian disease associated variants are present in every cell of the body, what cellular environments and mechanisms determine why some cells show a molecular or clinical phenotype when others do not? Why are some people or some tissues more susceptible to adverse events after taking medications than others? How does differential transcript expression impact disease progression? Can we identify gene regulatory states more susceptible to treatments and induce those states in cells? Which ligand-receptor-gene target relationship changes are important for disease progression and/or treatment?



Kidney Research

The Lasseigne Lab is currently engaged in multiple collaborative projects aimed at understanding the role of cell-cell communication, acute kidney injury, and cell state in polycystic kidney disease progression and how macrophage subpopulations respond to acute kidney injury with the goal of identifying disease mechanisms and potential drug targets for both through genomics approaches.

Recent publications/preprints:

Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse Pkd2 model gene expression reversion, Molecular Medicine, 2023.


Brain Research

Our lab has several collaborative projects leveraging single-cell and long-read genomics approaches to learn how different cell types contribute to Alzheimer’s, glioblastoma, and rare diseases impacting the brain through changes in cell-cell communication, transcription factor activity, and gene regulatory states.

Recent publications/preprints:

Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates, preprinted and under review, 2023.

Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles, preprinted and in revision, 2023.

Liquid biopsies in epilepsy: biomarkers for etiology, diagnosis, prognosis, and therapeutics, Human Cell, 2022.

Nucleic acid liquid biopsies in Alzheimer’s disease: current state, challenges, and opportunities, Heliyon, 2022

A role for GLUT3 in glioblastoma cell invasion that is not recapitulated by GLUT1, Cell Adhesion & Migration, 2021.


Drug Target and Repurposing Prioritization

We are dedicated to not only identifying the right drug for the right patient at the right time, but improving that process by prioritizing drug targets and repurposing candidates by assessing sex-biased adverse events, predicting side effects, identifying drugs that will perform similarly in preclinical models and patients, etc.

Recent publications/preprints:

Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse Pkd2 model gene expression reversion, Molecular Medicine, 2023.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes, preprinted and under review, 2023.

Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates, preprinted and under review, 2023.

Computational Advancements in Drug Repurposing for Cancer Combination Therapy Prediction, preprinted and under review, 2023.

Considerations and challenges for sex-aware drug repurposing, Biology of Sex Differences, 2022.


Methods, Tools, and Approaches for Cross-Condition Studies

The Lasseigne Lab develops open-source methods, tools, and approaches for studying how extrinsic (e.g., tissue, species) and intrinsic (e.g., transcript expressed, copy number) factors impact disease states and our interpretation of them.

Recent publications/preprints:

Shiny App for the visualization of TF activity in human GTEx data

Quantifying transcriptome diversity: a review, Briefings in Functional Genomics, 2023.

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis, preprinted, under review, and available on Bioconductor, 2023.

Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles, preprinted and in revision, 2023.

Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers, preprinted and under review, 2023.

CINmetrics: an R package for analyzing copy number aberrations as a measure of chromosomal instability, PeerJ and available on CRAN, 2023.

Ten simple rules for using public biological data for your research, PLoS Computational Biology, 2023.


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