Single-cell multiregion dissection of Alzheimer’s disease

Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution

Single-cell studies are frequently designed to compare across conditions such as health and disease. However, existing computational approaches typically rely on grouping cells into discrete populations before making comparisons, which can limit resolution for detecting state-dependent changes. Here, we introduce Kompot, a statistical framework for comparative analysis of multi-condition single-cell data. Kompot quantifies both differential abundance, capturing how cells redistribute across the phenotypic space, and differential expression, identifying condition-specific transcriptional changes that may be localized, heterogeneous, or oppositely regulated across states. By modeling cell density and gene expression as continuous functions over a shared cell-state representation, Kompot enables single-cell–resolution inference with principled uncertainty estimates, without requiring predefined clusters or cell types. Applying Kompot to aging murine bone marrow, we identified a continuum of shifts in hematopoietic stem cell and mature cell states, transcriptional remodeling of monocytes independent of compositional changes, and divergent regulation of oxidative stress response genes across cell types. We demonstrate the utility of Kompot in disease settings by identifying cell-state and gene expression changes associated with improved efficacy of combinatorial immunotherapy in melanoma. Additionally, Kompot enables multi-sample comparative analysis by accounting for sample-to-sample heterogeneity. By capturing both global and cell-state–specific effects of perturbation, the Kompot framework is broadly applicable to dissecting condition-specific effects in complex single-cell landscapes. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, R35GM147125, R01CA292932, T32GM136534, S10OD028685 The Mark Foundation for Cancer Research, https://ror.org/00v7th354, Endeavor Award Edward P. Evans Foundation, https://ror.org/03h22gm35, Discovery Research Grant Brotman Baty Institute, https://ror.org/03jxvbk42, Pilot Award

Embryo-scale reverse genetics at single-cell resolution

The maturation of single-cell transcriptomic technologies has facilitated the generation of comprehensive cellular atlases from whole embryos1–4. A majority of these data, however, has been collected from wild-type embryos without an appreciation for the latent variation that is present in development. Here we present the ‘zebrafish single-cell atlas of perturbed embryos’: single-cell transcriptomic data from 1,812 individually resolved developing zebrafish embryos, encompassing 19 timepoints, 23 genetic perturbations and a total of 3.2 million cells. The high degree of replication in our study (eight or more embryos per condition) enables us to estimate the variance in cell type abundance organism-wide and to detect perturbation-dependent deviance in cell type composition relative to wild-type embryos. Our approach is sensitive to rare cell types, resolving developmental trajectories and genetic dependencies in the cranial ganglia neurons, a cell population that comprises less than 1% of the embryo. Additionally, time-series profiling of individual mutants identified a group of brachyury-independent cells with strikingly similar transcriptomes to notochord sheath cells, leading to new hypotheses about early origins of the skull. We anticipate that standardized collection of high-resolution, organism-scale single-cell data from large numbers of individual embryos will enable mapping of the genetic dependencies of zebrafish cell types, while also addressing longstanding challenges in developmental genetics, including the cellular and transcriptional plasticity underlying phenotypic diversity across individuals.

GitHub - AIM-KannLab/BrainIAC | Giovanni Giulietti

One of the biggest hurdles in neuroimaging AI has always been the scarcity of labeled data, especially for rare conditions. But a recent study published in Nature Neuroscience introduces a potential solution: BrainIAC (Brain Imaging Adaptive Core). Unlike traditional models that are trained for one specific task, BrainIAC is a "foundation model", a large-scale AI model trained on vast amounts of data that can perform many different tasks and be adapted to specific applications. It was pretrained on a massive dataset of 48965 brain structural MRI sequences (T1w, T2w, FLAIR, T1CE) using self-supervised learning, allowing it to understand the "big picture" of brain anatomy across different ages and conditions. It has been tested across seven diverse and clinically meaningful applications:  - brain age prediction - IDH mutation classification - Mild Cognitive Impairment (MCI) classification - diffuse glioma overall survival prediction - MR sequence classification - time-to-stroke prediction - tumor segmentation  BrainIAC consistently outperformed traditional supervised learning approaches and other medical-specific models. The real "magic" happens in low-data scenarios. While traditional AI usually needs thousands of examples, BrainIAC can adapt to new tasks using only a few samples.  BrainIAC can be used for structural brain MRI analysis through two main methods: - Web Usage (BrainIAC platform): the easiest way to use BrainIAC, providing a user-friendly interface to upload structural brain MRI data and run inference. - Local usage (GitHub repository): for more advanced and customized analysis. It allows complete control over the data preprocessing, model training, and inference pipelines. It requires Python 3.9+ and NVIDIA GPU with CUDA 11.0+. 🔗 BrainIAC paper:    https://lnkd.in/dq6Gprgt 🔗 BrainIAC web platform https://lnkd.in/dpreadMp 🔗 BrainIAC GitHub repo: https://lnkd.in/duwSEg4V #MedicalAI #Neuroscience #HealthTech #BrainIAC #Radiology #Innovation #DeepLearning #PrecisionMedicine

Neuropeek — Accelerating the Neuro-AI convergence

Where brain data becomes shared knowledge. A community-driven platform federating neuroscience datasets, models, and tools to accelerate discovery.

E11 Bio | Moonshot Neuroscience

An FRO building scalable single-cell brain circuit mapping.

Benchmarking gene expression reconstruction from single-cell latent representations

Single-cell transcriptomics is typically modeled in low-dimensional latent representations that improve the signal-to-noise ratio of the data. Such representations underpin data integration, cell state discovery, and perturbation prediction, with applications ranging from large-scale organ atlases to latent trajectory modeling. Recent virtual cell approaches further leverage these representations to predict cellular responses as distributional shifts in latent space. Each of these applications ultimately requires faithful gene expression reconstruction from latent spaces for biological interpretation, enabling gene-level analysis of predicted perturbed or batch-corrected cells. Yet representation choice is typically treated as an implementation detail rather than a primary modeling decision, with no systematic evaluation of how well latent representations support gene expression reconstruction. Here, we introduce ReconEval, a benchmark for evaluating gene expression reconstruction from single-cell latent spaces. We benchmark two classes of latent representations: end-to-end trained models such as PCA, autoencoders, and variational autoencoders, and pretrained single-cell foundation model embeddings coupled to newly trained decoders. Reconstruction is evaluated both directly and after latent-space perturbation prediction. Across perturbational and observational datasets totaling over 100 million cells, our metric suite quantifies statistical fidelity; biological signal preservation, including differential expression, coexpression, cell-cycle structure, cytokine response and pathway activity; and perturbation-specific effects. We find that autoencoders achieve the strongest stand-alone reconstruction at low dimensionality, while variational regularization does not improve generalization in reconstruction. Frozen foundation model embeddings retain recoverable gene-level information, with reconstruction quality depending strongly on decoder architecture and pretraining objective. In latent perturbation modeling, high-dimensional PCA matches foundation model embeddings, while low-dimensional AE embeddings are optimal for flow-based generative models. Overall, reconstruction depends critically on the interplay between representation and downstream model, and simpler representations can outperform complex alternatives given appropriate capacity. Our benchmark establishes reconstruction as a critical evaluation axis for single-cell foundation models. We envision it improving the biological interpretability of latent-space modeling, a prerequisite for future virtual cell models to be validated by domain experts and grounded in biology. ### Competing Interest Statement F.J.T. consults for Immunai, CytoReason, Valinor Industries, Bioturing and Phylo Inc., and has ownership interest in RN.AI Therapeutics, Dermagnostix, and Cellarity. The remaining authors declare no competing interests.

nubrain - A foundation model for neural decoding

Building the world's largest dataset of human brain activity.

PTI-125 Reduces Biomarkers of Alzheimer’s Disease in Patients

The most common dementia worldwide, Alzheimer’s disease is often diagnosed via biomarkers in cerebrospinal fluid, including reduced levels of Aβ1-42, and increases in total tau and phosphorylated tau-181. Here we describe results of a Phase 2a study of a promising new drug candidate that significantly reversed all measured biomarkers of Alzheimer’s disease, neurodegeneration and neuroinflammation. PTI-125 is an oral small molecule drug candidate that binds and reverses an altered conformation of the scaffolding protein filamin A found in Alzheimer’s disease brain. Altered filamin A links to the α7-nicotinic acetylcholine receptor to allow Aβ42’s toxic signaling through this receptor to hyperphosphorylate tau. Altered filamin A also links to toll-like receptor 4 to enable Aβ-induced persistent activation of this receptor and inflammatory cytokine release. Restoring the native shape of filamin A prevents or reverses filamin A’s linkages to the α7-nicotinic acetylcholine receptor and tolllike receptor 4, thereby blocking Aβ42’s activation of these receptors. The result is reduced tau hyperphosphorylation and neuroinflammation, with multiple functional improvements demonstrated in transgenic mice and postmortem Alzheimer’s disease brain.

Precision Medicine in Neuroscience: Tools, Translation, and Implementation: A Workshop

Precision medicine approaches are rapidly transforming neuroscience, driven by advances in genetics, neuroimaging, biomarkers, and data science. These tools enable more refined disease classification, improved diagnosis, and treatments tailored to individual patients across neurological and psychiatric disorders. However, challenges remain in translating these advances into routine research and clinical practice. On March 4–5, the National Academies’ Forum on Neuroscience and Nervous System Disorders, in collaboration with the Forum on Drug Discovery, Development, and Translation and the Roundtable on Genomics and Precision Health, will host a workshop exploring opportunities, challenges, and strategies for integrating precision medicine into neuroscience research and care.

Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data

Bulk RNA-seq enables effective profiling of large cohorts and complex experimental designs, but current single-cell-informed deconvolution methods incompletely resolve closely related cell phenotypes, do not scale efficiently to large single-cell datasets, or fail to account for cellular content not represented in the reference. Here, we present Rectangle, an scverse Python framework for single-cell-informed deconvolution of bulk RNA-seq data.

VAPOR: A variational autoencoder with transport operators to disentangle cellular gene expression dynamics of co-occurring biological processes in time and space

Abstract Single-cell and spatial transcriptomics enable the analysis of cellular states and dynamics in gene expression, revealing how diverse biological processes relate to these states over time and space. To study these dynamics, trajectory inference methods order cells along computationally inferred paths to reconstruct gradual transitions in cell states. However, by encouraging smooth and continuous trajectories, these approaches tend to conflate co-occurring processes-such as proliferation, maturation, and spatial organization-that are jointly reflected in gene expression, potentially overlooking process-specific gene expression dynamics. To address this, we developed VAPOR, which integrates a variational autoencoder with transport operators to model and disentangle cellular gene expression dynamics for potentially co-occurring biological processes. VAPOR inputs single-cell (or spatial) gene expression data into a variational autoencoder (VAE) to learn the latent states of cells and then models their latent dynamics as an ordinary differential equation. The latent dynamics are further decomposed into process-specific components parameterized by transport operators (TOs) and their corresponding process weights. Each TO defines a process-specific dynamics, and its weight for each cell quantifies the process's contribution to the cell dynamics. After assessment by simulation studies, we applied VAPOR with benchmarking to real data, including time-course scRNA-seq from postconceptual human brain development, spatial transcriptomics of the mouse hippocampus, and cross-species scRNA-seq spanning human and macaque first-trimester forebrain development. In these applications, VAPOR has identified a variety of temporal and spatial co-occurring processes, such as cell cycle, gliogenesis, neurogenesis, and neuronal migration, along with associated dynamic genes, including those species-specific to human and macaque development. VAPOR is available as an open-source tool for general-purpose use. ### Competing Interest Statement The authors have declared no competing interest.

Hierarchical classification of immune cell transcriptomes at population-scale

Accurate immune cell classification is essential for interpreting single-cell RNA sequencing (scRNA-seq) data. However, progress is constrained by the lack of independent, high-resolution benchmarks, as the routine integration of datasets introduces statistical dependencies that artificially inflate model generalizability. Here, we present the single-cell universal classification omnibus (Suco), a resource of independent, uniform expert annotations, and Compocyte, a modular hierarchical classifier. Together, they establish a framework designed for the scale of human population immunology. This approach substantially outperforms existing classifiers while facilitating expert review of ambiguous annotations. Applying Compocyte across 50 studies, including three newly generated datasets, we classified 15.6 million leukocytes from 3,965 patients. Within this expansive cohort, we identified a new tumor-associated resorptive macrophage phenotype, a non-canonical monocyte subtype in subclinical cytokine release syndrome, and the programmatic erosion of T cell memory stemness across metastatic sites. Suco and Compocyte thus provide a generalizable architecture and benchmark capable of sustaining high-resolution annotation across massive clinical cohorts. ### Competing Interest Statement CMR has consulted regarding oncology drug development with Amgen, AstraZeneca, Daiichi Sankyo, Genentech, Merck, and Novartis, and has received licensing and royalty payments for DLL3-directed therapeutics. T.W. reports stock ownership for Roche, Astra Zeneca, Bayer, Innate Pharma, Kyntra, Illumina, 10x Genomics, and Merck KGaA as well as research funding from Atrandi Biosciences, Vilnius, Lithuania; CanVirex AG, Basel Switzerland; and Institut fuer Klinische Krebsforschung GmbH, Frankfurt, Germany, and travel funding from Roche, Basel, Switzerland. S.Z. reports advisory board membership and honoraria from Amgen, Astellas, AstraZeneca, Bayer, Bristol-Myers Squibb, Daiichi Sankyo, Eisai, EUSA, Gilead, Ipsen, Johnson&Johnson, Lilly, MedSir, Medtoday, Merck, MSD, Novartis, Pfizer, Roche, Sanofi Aventis, StreamedUp, Urotrials, Urotube, Zentiva and resarch funding from Eisai. S.Z. reports clinical trial support from Amgen, AstraZeneca, AVEO, Bayer, Biontech, Bristol-Myers Squibb, Calithera, Exelixis, Gilead, Lilly, MSD, Novartis, Pfizer, Roche, Seagen/Astellas, Urotrials and travels & conference support from Amgen, Astellas, AstraZeneca, Bayer, EISAI, Ipsen, Johnson&Johnson, Merck, MSD, Pfizer. All remaining authors declare no relevant competing interests. Spanish Association Against Cancer, PI049999 Federal Ministry of Research, Technology and Space, 001001KT2322 National Cancer Institute, R35 CA263816 National Cancer Institute, U24 CA213274 National Cancer Institute, P30 CA008748 Research Council of Lithuania, P-MIP-24-93

OpenAstrocytes – Forecast Research

Astrocytes make up about a third of the cells in your brain, and form a second, distinct brain network from the more-studied neuronal network. As neuroscience has discovered in only the last few years, the astrocyte network carries out extremely important computations, integrating information from across different brain networks, and using this information to drive feedback to neurons that controls physiological state and maintains long-term homeostasis.

Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon’s efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.

Reducing Amyloid-Related Alzheimer's Disease Pathogenesis by a Small Molecule Targeting Filamin A

PTI-125 is a novel compound demonstrating a promising new approach to treating Alzheimer's disease (AD), characterized by neurodegeneration and amyloid plaque and neurofibrillary pathologies. We show that the toxic signaling of amyloid-β42 (Aβ42) by the α7-nicotinic acetylcholine receptor (α7nAChR), which results in tau phosphorylation and formation of neurofibrillary tangles, requires the recruitment of the scaffolding protein filamin A (FLNA). By binding FLNA with high affinity, PTI-125 prevents Aβ42's toxic cascade, decreasing phospho-tau and Aβ aggregates and reducing the dysfunction of α7nAChRs, NMDARs, and insulin receptors. PTI-125 prevents Aβ42 signaling by drastically reducing its affinity for α7nAChRs and can even dissociate existing Aβ42–α7nAChR complexes. Additionally, PTI-125 prevents Aβ-induced inflammatory cytokine release by blocking FLNA recruitment to toll-like receptor 4, illustrating an anti-inflammatory effect. PTI-125's broad spectrum of beneficial effects is demonstrated here in an intracerebroventricular Aβ42 infusion mouse model of AD and in human postmortem AD brain tissue.

Gene regulatory networks: from correlative models to causal explanations

Gene regulatory networks (GRNs) explain how the genome controls cellular behaviour and tissue morphogenesis, serving to connect molecular mechanism to functional output. Single-cell technologies now provide descriptions of these networks with unprecedented detail, but this advance has also revealed gene regulatory systems that are too complex for our existing conceptual frameworks. GRNs, which should provide mechanistic explanations, are increasingly reduced to statistical correlations — ‘hairballs’ that fail to capture molecular causation. Here, we explore why this dilemma exists and propose a path forward. We argue that methods in ‘representation learning’ can be used to model GRNs, without needing to capture every molecular detail. For this framework, we advocate three linked principles: models must be inherently mechanistic, with structures grounded in cellular and evolutionary biology; molecular principles and constraints must be used to reduce the solution space for learning GRN models; and more sophisticated forms of experimental perturbation and synthetic biological engineering are needed to train models and test predictions. By reimagining GRNs through these principles, we can bridge the gap from data abundance to new conceptual understanding.