Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

Abstract

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

“…This points out the necessity of conducting post-hoc regression analyses for identifying the most optimal mixtures of dictionaries. In Ji et al ( 2014 ) and Timonidis et al ( 2020 ), predictions of projection patterns as sparse linear combinations of gene expression patterns were shown to be significant when representing both modalities at the level of brain areas. However, Linked ICA provides an advantage in terms of interpretation, since reconstructing both data modalities is implicitly modelled by the method instead of requiring post-hoc analyses.…”

“…Additionally, the meta-analytic steps of our analysis have been designed and tested in the form of a Jupyter Notebook and have been published online with their descriptions at Github. The Github Notebook has been incorporated in the Connectomic-Composition-Predictor (CCP), a Neuroinformatics-related tool that we developed in our previous work (Timonidis et al 2020 ). Regarding the modifications made to Linked ICA in this work, a potential user can consult the steps described in Supplementary Material Section 1.1 .…”

FriendlyClearMap: An optimized toolkit for mouse brain mapping and analysis

“…Being able to quantify numbers and distribution of different classes of inhibitory neurons is relevant for the study of neuropathological phenotypes, where dysfunction of GABAergic neuron classes is a common feature 33 . In those studies, whole-brain datasets can help identify especially hard-hit brain regions and identify functional networks that are likely to be especially impaired 34 . In order to visualize the labelled neurons, we used reporter mice expressing tdTomato, which we then visualized with an anti-RFP antibody staining and whole-hemisphere iDISCO+ clearing.…”

FriendlyClearMap: an optimized toolkit for mouse brain mapping and analysis

“…Being able to quantify numbers and distribution of different classes of inhibitory neurons is relevant for the study of neuropathological phenotypes, where dysfunction of GABAergic neuron classes is a common feature [ 33 ]. In those studies, whole-brain datasets can help identify especially hard-hit brain regions and identify functional networks that are likely to be especially impaired [ 34 ]. In order to visualize the labeled neurons, we used reporter mice expressing tdTomato, which we then visualized with an anti-RFP antibody staining and whole-hemisphere iDISCO+ clearing.…”