Mariko F. Taga

The paper develops a post-processing workflow combining Xenium spatial technology with 4i imaging to enable multi-omic mapping of post-mortem human brain tissue, focusing on pathological microenvironments.

This study shows how non-coding variants in CD33 affect Alzheimer's risk by altering CD33M dimerization and interactions with clusterin and Aβ, impacting microglial function.

Spacelink integrates spatial transcriptomics with genetics to identify spatially variable genes linked to disease, showing improved detection power and disease informativeness.

The paper uses automated image segmentation to identify CD74high microglia associated with cognitive decline in AD, linking transcriptomic and morphological features.

This study identifies CD45 as a sialic acid-dependent binding partner of CD33, showing that their interaction contributes to Alzheimer's disease susceptibility through impaired microglial function.

The paper introduces a proteomic tool to identify protein markers for human microglial states and validates their utility in purification and differentiation.

The paper uses single-cell data to identify factors regulating an Alzheimer's disease-related microglial signature and proposes a new analytic framework for microglial classification.

The paper identifies a novel astrocyte state linked to cognitive decline in Alzheimer's disease and validates its regulators through multi-modal analysis and mechanistic experiments.

This study identifies 236 MS susceptibility variants and highlights neuronal and glial dysfunction as key factors in MS susceptibility.

The paper identifies a SERPINA3-expressing astrocytic state in the human neocortex near neuritic plaques using spatial transcriptomic analysis.

The paper identifies disease-specific cell signatures in non-lesional brain tissue of MS patients using single-cell and spatial transcriptomics.

The paper identifies two distinct aging trajectories in the human brain linked to Alzheimer's disease or alternative aging, using single-nucleus RNA-seq data and causal modeling.

The paper maps cellular changes in the aging human brain and identifies altered multicellular communities in Alzheimer's disease using single nucleus RNA-seq and bulk RNA-seq data.

This study identifies disease-enriched microglial subsets and validates compounds that modulate their states using single-cell RNA sequencing and in vitro experiments.

The paper evaluates methods for estimating cell-type proportions in brain tissue using paired transcriptomic and immunohistochemistry data, showing some algorithms provide accurate estimates.

Molecular network analysis of RNA sequencing data from the aging human cortex identifies new Alzheimer's and cognitive decline genes.

The paper identifies gene modules in human frontal cortex microglia associated with beta-amyloid and tau pathology in Alzheimer's disease.