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Single-cell atlas of in vitro neurodevelopment

What does a dish of differentiating neurons actually contain, cell by cell?

We build single-cell transcriptomic atlases of iPSC-derived neuronal differentiation, integrating 2D and 3D models with fetal reference data, so that in vitro systems can be judged against the developing human brain rather than against each other.

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Why this matters

Every group that differentiates stem cells into neurons ends up with a dish of something. What that something is, and how close it sits to a real developing brain, is usually argued from a handful of marker genes. An atlas replaces that argument with a coordinate system.

What we do

We profile differentiation at single-cell resolution across protocols and time points, then integrate those datasets with fetal reference data so that any new experiment can be projected onto a shared reference rather than interpreted on its own. We also examine how heritability is expressed in these cells.

UMAP- 2.8 million cells
UMAP- 2.8 million cells
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Epigenetic modifiers in neuronal development

How does losing a chromatin regulator change the course of a developing neuron?

Loss-of-function mutations in genes of the epigenetic machinery are a recurrent cause of neurodevelopmental and psychiatric disorders. We follow patient-derived and CRISPR-engineered lines through neuronal differentiation using single-cell transcriptomics.

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The question

Mutations in the epigenetic machinery turn up again and again in neurodevelopmental and psychiatric disorders. They rarely break a single pathway. They shift the probability that a cell takes one developmental route rather than another.

The approach

We take patient-derived and CRISPR-engineered lines through differentiation and profile them at single-cell resolution, so we can look for the point at which trajectories diverge rather than only the difference at the end.

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Genetic background effects in engineered models

Is a patient's own cell line worth more than the same mutation engineered into a healthy one?

Using CRISPR-Cas9 we compare neuronal cells carrying a mutation in a patient's own genetic background against wild-type and engineered counterparts across different backgrounds, to test how much of a disease phenotype the surrounding genome accounts for.

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A practical question, not only a biological one

Much of the field assumes that engineering a mutation into a healthy reference line is the cleaner experiment, because everything else is held constant. That assumption is worth testing directly.

What we compare

Patient-derived lines carrying a variant, isogenic engineered lines carrying the same variant, and wild-type controls, differentiated side by side across several genetic backgrounds.

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Molecular variation in rare disease iPSCs

Do cells from people with rare genetic disease differ measurably from controls?

Analysis of molecular data from stem cell disease models across 210 individuals with rare genetic diseases, building on earlier work with the Human Induced Pluripotent Stem Cell Initiative, to establish how far a disease genotype is visible in an undirected cell.

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The cohort

Molecular data from stem cell disease models across 210 individuals with rare genetic diseases, building on the Human Induced Pluripotent Stem Cell Initiative.

The question

Before any differentiation is attempted, is a disease genotype already visible in the transcriptome of an undirected cell? The answer determines how much of disease modelling has to happen downstream.

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Shared genetics of sleep and neuropsychiatric traits

Which genetic signals are shared between sleep conditions and psychiatric disorders?

Genetic and multi-omic analysis of psychiatric and neurological disorders, including the shared genetic determinants between sleep conditions and neuropsychiatric disorders, connecting population-scale association signals to the cellular models built elsewhere in the group.

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From population to cell

Association studies tell us where in the genome a signal sits. They do not tell us which cell type it acts in, or when. This topic exists to carry those signals into the cellular models the rest of the group builds.

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Morphological and functional characterisation of neurons

What a neuron looks like, measured alongside what it expresses.

Developing methods that characterise iPSC-derived neuronal cells morphologically and functionally, so that imaging phenotypes can be joined to transcriptomic state in the same cells rather than compared across separate experiments.

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Same cell, two measurements

Morphology and expression are usually measured in different experiments and compared as averages. Measuring both in the same cells turns a correlation between populations into a relationship between observations.

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Transcriptional dysregulation in intellectual disability

One gene, one disrupted regulatory programme, a measurable cellular consequence.

Work on gene expression dysregulation in schizophrenia and intellectual disability, including how INTS6 loss of function disrupts transcriptional regulation in mild intellectual disability.

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A single gene as a way in

Following one gene from a loss-of-function variant through to a disrupted regulatory programme gives a complete chain of reasoning that broader studies can then be checked against.

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