Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common form of dementia, accounting for an estimated 60-70% of all cases worldwide. Understanding how gene regulation changes in specific brain cell types during disease progression is essential to uncovering the molecular mechanisms of AD. Recent research has introduced new methods to extend Gene Regulatory Network (GRN) inference with ATAC-seq in addition to RNA-seq, including ScReNI.

This paper investigates whether integrating chromatin accessibility into GRN inference can help explain gene regulatory changes in specific brain cell types during AD progression by evaluating whether ScReNI's ATAC-derived terms improve biological support beyond RNA-only inference. Using a Python reimplementation, we decompose the published ScReNI formula and assess formula variants on the mouse retina development benchmark with ChIP-Atlas precision and network-clustering ARI. We then apply the same component analysis to SEA-AD MTG data and evaluate whether literature reported AD-related microglia genes survive feature selection.

The results suggest that removing the regulator-locus term and using TF-specific target-peak attribution improves performance of the ScReNI weight formula on the mouse retina development dataset. However, before ScReNI can support strong AD-specific regulatory claims in SEA-AD, feature selection must retain disease-relevant regulators and validation must be performed using human brain cell-type-specific benchmarks. ... Read more

ScReNI [21] is a recently developed algorithm that aims to infer gene regulatory networks (GRNs) of single cells based on both single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data. Because of its novelty, not much is known about its internal mechanisms, which we aim to investigate in this paper.

Specifically, this work compares the highly variable gene (HVG) selection strategy used in ScReNI with a newly proposed approach: type-specific HVG selection. Instead of selecting the top HVGs globally from the entire dataset, we propose selecting the top HVGs within each cell type and inferring the GRN of each cell using only genes specific to its cell type. The comparison is conducted across multiple structural and biological metrics, and the type-specific selection approach shows overall improved performance compared to the original method. ... Read more

Alzheimer’s disease (AD) does not affect all brain cells in the same way, and a
cell’s dysfunction is thought to involve changes in how its genes are regulated rather
than only changes in their expression. Gene regulatory networks (GRNs) model these
regulatory decisions, and recent single-cell methods can infer a separate network for
each individual cell. How such cell-specific networks change in disease, and whether
any changes are shared across cell types or unique to particular ones, remains largely
unexplored. This work asks which regulatory relationships differ between AD and
healthy cells, and to what extent these differences are cell-type specific. To address
this, the cell-specific GRN method ScReNI is reimplemented in Python and applied
to paired single-nucleus RNA and ATAC data from the SEA-AD cohort (27 donors),
inferring one network per cell for microglia and an excitatory-neuron subclass (L2/3
IT). A differential pipeline then compares the networks against disease severity at
the level of individual transcription-factor-to-target edges and of co-regulated gene
modules, complemented by a module-preservation test, treating the donor as the unit of replication. The inferred networks change relatively little with disease: no edge survives stringent correction and weight-based filtering, the module co-regulation structure is preserved, and no module-specific shift is detected in either cell type. The one robust signal is a modest, severity-graded decline in overall regulatory activity in L2/3 IT neurons that persists after adjusting for sequencing depth and is absent in microglia. The results are best read as an absence of strong evidence rather than evidence of absence, motivating larger cohorts and broader gene panels in future work. ... Read more

Alzheimer's disease progresses in a spatially heterogeneous manner across the brain, yet how the gene regulatory networks (GRNs) of individual cells vary across space in affected tissue remains largely unstudied. This thesis investigates whether changes in the spatial location of cells in human Alzheimer's disease tissue can be related to changes in GRN structure and the activity of key regulators. Cell-specific GRNs were inferred from paired single-nucleus RNA and ATAC data using pyScReNI, a Python port of the ScReNI algorithm, and projected onto MERFISH spatial coordinates using Tangram, for four cell subtypes across ten overlapping donors from the Seattle Alzheimer's Disease Brain Cell Atlas. First, the spatial mapping was validated as a feasible proof of concept, placing 22-29% of held-out cells within 500μm of their true location. GRN structure was found to be non-randomly organised in tissue space: no signal was present at the level of the whole network, but a subset of leading GRN components was significantly spatially autocorrelated, and the resulting GRN-defined clusters were spatially contiguous beyond chance. This spatial signal was carried by specific regulators concentrated in the neuronal subtypes and absent in the glial subtypes. The most spatially autocorrelated of these, AC106845.1, reached roughly 2.4 times the subtype-median spatial autocorrelation, followed by ADAM28 and FAM189A2, and these regulators operated through an on and off change in activity rather than a rewiring of regulatory targets. A directional but non-significant association with disease severity was observed, which the ten-donor cohort left underpowered. These findings show that single-cell GRNs can be inferred and spatially analysed at a resolution finer than previously available methods, and that their structure is spatially organised by specific regulators in Alzheimer's disease tissue. ... Read more

Gene regulatory networks (GRNs) inferred at single-cell resolution offer insight into regulatory mechanisms underlying complex diseases such as Alzheimer's disease, but the reliability of the GRNs produced by methods such as ScReNI is not fully explored. This work investigates the stability and reliability of GRNs inferred by a Python reimplementation of ScReNI applied to the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) and a mouse retinal dataset. Three experiments were conducted. First, gene-level prediction accuracy, measured via random forest out-of-bag (OOB) R², was found to vary across genes and to be driven primarily by expression level rather than network position. Second, two main candidate cell-level reliability metrics were evaluated against precision and recall: OOB R² showed no significant association with precision once network density and expression were statistically controlled for, whereas edge weight variance across repeated inference runs retained significant correlation with both, making it the more promising candidate metric identified. Third, comparing the GRNs of cells with high versus low edge weight variance within cell types revealed that such networks share similar topological structure but diverge substantially in edge weight magnitude. These findings indicate that out-of-bag accuracy alone is an insufficient proxy for GRN reliability, and that edge weight variance, while more informative, does not yet provide a fully cell-type-consistent reliability metric, motivating further work on alternative approaches to quantifying single-cell GRN stability. ... Read more

Deep Neural Networks (DNNs) are renowned for their high accuracy and versatility, which has led to their application in many fields of research, including biology. However, this accuracy often comes at the expense of interpretability, making it challenging to reason about the inner workings of most DNNs. Particularly in biological research, understanding the mechanisms behind specific outcomes is highly valuable. To elucidate the latent space of DNNs in the context of cancer biology, we introduce GONNECT: a Gene Ontology-derived Neural Network for Explainable Cancer Typing. GONNECT incorporates biological prior knowledge from the Gene Ontology (GO) directly into its network architecture, enabling interpretability through model structure. Using an autoencoder framework, we evaluate GONNECT as both encoder and decoder module and demonstrate its ability to learn which biological processes are distinctive for different cancer types. Furthermore, we show how a variant including soft links (GONNECT-SL) can expand on current knowledge by proposing new interactions between biological processes. GONNECT is flexible both in the amount of prior knowledge it incorporates and the set of input genes, and can potentially be applied in modeling of gene perturbation effects and drug target discovery. ... Read more

Single-cell RNA sequencing (scRNAseq) is a measuring technique of gene expressions in single cells that has allowed researchers to tackle Alzheimer’s disease (AD) in many ways. Single-cell data has been joined with machine learning to classify brain cells as affected by AD. However, not much is known regarding the usage of such classification models in a spatial setting. This paper analyzes how models trained on scRNAseq data can be used to find AD properties of single cells when measuring them with spatially resolved transcriptomics. With that we study the hypothesis that cells labeled as affected by the disease should appear closer to amyloid plaques, than those that are unaffected. To find out if this holds, three models are used to classify single cells spatially and their predictions are analyzed. Two single-cell datasets are used for training, each giving a drastically different classification outcome. The models do not come to a consensus on the hypothesis’ validity either, as the analysis finds no significant correlation between the variables. ... Read more

Alzheimer's Disease (AD) is a complex heterogeneous disease and is the leading cause of dementia around the world. Treatment options remain limited and the underlying mechanisms are not yet fully understood. To get more insight on this celular level, single-cell gene expression data can be used. It has proven to be effective with machine learning for tasks like cell type classification. While prior studies have explored AD classification using scRNA-seq, this has only been a binary classification. Severity of AD is classified using multiple measures, ranging from cognitive ability scores, to neuro pathological measures. This research explores the possibility of expanding the binary prediction of AD by including these measures for AD severity. In addition, given that these measures are associated, we also investigate if Multi Task Learning (MTL) models can improve the predictions by learning multiple AD related data points. If successful, this approach can give additional analysis into key tasks, genes and/or cells (sub)types that drive the models, which would lead to more possibilities for personalized treatment options, alongside more insight into the development of AD in the brain. We used a three-layer neural network architecture alongside a translation from cellular level to individual level to make individual-level predictions. Results show that Cognitive Ability can be classified best, but overal performance is only slightly above Naive Bayes. Furthermore, MTL does not appear to have any measurable positive effect on scores compared to single task models. A link to the github repository is available at \url{https://github.com/WillemDieleman/ADseverityCSE3000}. ... Read more