A STATISTICAL FRAMEWORK FOR THE MODELING OF THE CONTINUOUS PROGRESSION OF ALZHEIMER'S DISEASE.

Throughout an organism's life, a multitude of biological systems transition through complex biophysical processes. These processes serve as indicators of the underlying biological states. Inferring these latent unobserved states is a key problem in modern biology and neuroscience. Unfortunately, in many experimental setups we can at best obtain snapshots of the system at different times for different individuals, and one major challenge is the one of reconciling those measurements. This formalism is particularly relevant in the study of Alzheimer's Disease (AD) progression, in which we observe in brain donors the aggregation of pathological proteins but the underlying disease state is unknown. The progression of AD can be modeled by assigning a latent score - termed pseudotime - to each pathological state, creating a pseudotemporal ordering of donors based on their pathological burden. This paper proposes a hierarchical Bayesian framework to model AD progression using detailed quantification of multiple AD pathological proteins from the Seattle AD Brain Cell Atlas consortium (SEA-AD). Inspired by biophysical models, we model pathological burden as an exponential process. Theoretical properties of the model are studied, by using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, and show the effectiveness of our approach with multiple simulation studies across data conditions. Applying the methodology to SEA-AD brain data, we infer pseudotime for each donor and order them by pathological burden. Finally, we analyze the information within each pathological feature and utilize it to refine the model by focusing on the most informative pathologies. This lays the groundwork for suggesting future experimental design approaches.

MultiVI: deep generative model for the integration of multimodal data.

Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data, even for cells for which one or more modalities are missing. It is available at scvi-tools.org .

Integrated multimodal cell atlas of Alzheimer's disease.

Alzheimer's disease (AD) is the most common cause of dementia in older adults. Neuropathological and imaging studies have demonstrated a progressive and stereotyped accumulation of protein aggregates, but the underlying molecular and cellular mechanisms driving AD progression and vulnerable cell populations affected by disease remain coarsely understood. The current study harnesses single cell and spatial genomics tools and knowledge from the BRAIN Initiative Cell Census Network to understand the impact of disease progression on middle temporal gyrus cell types. We used image-based quantitative neuropathology to place 84 donors spanning the spectrum of AD pathology along a continuous disease pseudoprogression score and multiomic technologies to profile single nuclei from each donor, mapping their transcriptomes, epigenomes, and spatial coordinates to a common cell type reference with unprecedented resolution. Temporal analysis of cell-type proportions indicated an early reduction of Somatostatin-expressing neuronal subtypes and a late decrease of supragranular intratelencephalic-projecting excitatory and Parvalbumin-expressing neurons, with increases in disease-associated microglial and astrocytic states. We found complex gene expression differences, ranging from global to cell type-specific effects. These effects showed different temporal patterns indicating diverse cellular perturbations as a function of disease progression. A subset of donors showed a particularly severe cellular and molecular phenotype, which correlated with steeper cognitive decline. We have created a freely available public resource to explore these data and to accelerate progress in AD research at SEA-AD.org.

Genetic and epigenetic coordination of cortical interneuron development.

One of the hallmarks of the cerebral cortex is the extreme diversity of interneurons1-3. The two largest subtypes of cortical interneurons, parvalbumin- and somatostatin-positive cells, are morphologically and functionally distinct in adulthood but arise from common lineages within the medial ganglionic eminence4-11. This makes them an attractive model for studying the generation of cell diversity. Here we examine how developmental changes in transcription and chromatin structure enable these cells to acquire distinct identities in the mouse cortex. Generic interneuron features are first detected upon cell cycle exit through the opening of chromatin at distal elements. By constructing cell-type-specific gene regulatory networks, we observed that parvalbumin- and somatostatin-positive cells initiate distinct programs upon settling within the cortex. We used these networks to model the differential transcriptional requirement of a shared regulator, Mef2c, and confirmed the accuracy of our predictions through experimental loss-of-function experiments. We therefore reveal how a common molecular program diverges to enable these neuronal subtypes to acquire highly specialized properties by adulthood. Our methods provide a framework for examining the emergence of cellular diversity, as well as for quantifying and predicting the effect of candidate genes on cell-type-specific development.

A New Era for Space Life Science: International Standards for Space Omics Processing.

Space agencies have announced plans for human missions to the Moon to prepare for Mars. However, the space environment presents stressors that include radiation, microgravity, and isolation. Understanding how these factors affect biology is crucial for safe and effective crewed space exploration. There is a need to develop countermeasures, to adapt plants and microbes for nutrient sources and bioregenerative life support, and to limit pathogen infection. Scientists across the world are conducting space omics experiments on model organisms and, more recently, on humans. Optimal extraction of actionable scientific discoveries from these precious datasets will only occur at the collective level with improved standardization. To address this shortcoming, we established ISSOP (International Standards for Space Omics Processing), an international consortium of scientists who aim to enhance standard guidelines between space biologists at a global level. Here we introduce our consortium and share past lessons learned and future challenges related to spaceflight omics.

Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling.

ATAC-seq has become a leading technology for probing the chromatin landscape of single and aggregated cells. Distilling functional regions from ATAC-seq presents diverse analysis challenges. Methods commonly used to analyze chromatin accessibility datasets are adapted from algorithms designed to process different experimental technologies, disregarding the statistical and biological differences intrinsic to the ATAC-seq technology. Here, we present a Bayesian statistical approach that uses latent space models to better model accessible regions, termed ChromA. ChromA annotates chromatin landscape by integrating information from replicates, producing a consensus de-noised annotation of chromatin accessibility. ChromA can analyze single cell ATAC-seq data, correcting many biases generated by the sparse sampling inherent in single cell technologies. We validate ChromA on multiple technologies and biological systems, including mouse and human immune cells, establishing ChromA as a top performing general platform for mapping the chromatin landscape in different cellular populations from diverse experimental designs.

Origin and Segmental Diversity of Spinal Inhibitory Interneurons.

Motor output varies along the rostro-caudal axis of the tetrapod spinal cord. At limb levels, ∼60 motor pools control the alternation of flexor and extensor muscles about each joint, whereas at thoracic levels as few as 10 motor pools supply muscle groups that support posture, inspiration, and expiration. Whether such differences in motor neuron identity and muscle number are associated with segmental distinctions in interneuron diversity has not been resolved. We show that select combinations of nineteen transcription factors that specify lumbar V1 inhibitory interneurons generate subpopulations enriched at limb and thoracic levels. Specification of limb and thoracic V1 interneurons involves the Hox gene Hoxc9 independently of motor neurons. Thus, early Hox patterning of the spinal cord determines the identity of V1 interneurons and motor neurons. These studies reveal a developmental program of V1 interneuron diversity, providing insight into the organization of inhibitory interneurons associated with differential motor output.

Spinal Inhibitory Interneuron Diversity Delineates Variant Motor Microcircuits.

Animals generate movement by engaging spinal circuits that direct precise sequences of muscle contraction, but the identity and organizational logic of local interneurons that lie at the core of these circuits remain unresolved. Here, we show that V1 interneurons, a major inhibitory population that controls motor output, fractionate into highly diverse subsets on the basis of the expression of 19 transcription factors. Transcriptionally defined V1 subsets exhibit distinct physiological signatures and highly structured spatial distributions with mediolateral and dorsoventral positional biases. These positional distinctions constrain patterns of input from sensory and motor neurons and, as such, suggest that interneuron position is a determinant of microcircuit organization. Moreover, V1 diversity indicates that different inhibitory microcircuits exist for motor pools controlling hip, ankle, and foot muscles, revealing a variable circuit architecture for interneurons that control limb movement.

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons.

Documenting the extent of cellular diversity is a critical step in defining the functional organization of tissues and organs. To infer cell-type diversity from partial or incomplete transcription factor expression data, we devised a sparse Bayesian framework that is able to handle estimation uncertainty and can incorporate diverse cellular characteristics to optimize experimental design. Focusing on spinal V1 inhibitory interneurons, for which the spatial expression of 19 transcription factors has been mapped, we infer the existence of ~50 candidate V1 neuronal types, many of which localize in compact spatial domains in the ventral spinal cord. We have validated the existence of inferred cell types by direct experimental measurement, establishing this Bayesian framework as an effective platform for cell-type characterization in the nervous system and elsewhere.