Biology subjects

Pasarkar, A. P.

Publications and source records attributed to Pasarkar, A. P..

4 recordsLinked to original sources

bioRxiv2025

DENDRO: Recovery and denoising of whole-tree dendritic voltagefrom 2D voltage movies

The ability to image voltage at high spatiotemporal resolution across an entire dendritic tree would represent a major advance in systems and circuit neuroscience. Recent advances in genetically encoded voltage indicators (GEVIs) have brought this possibility closer to reality. However, due to fundamental tradeoffs between imaging speed, resolution, SNR, and volume, this goal has remained out of reach. Here we develop a computational method that fuses 3D anatomical information with 2D voltage video data, yielding full time-varying 3D voltage estimates. Our method, termed DENDRO, comprises two steps. In step one, we use the anatomical data to build a microscope model which maps from voltages along the tree to observed fluorescence at the imaging plane. By exploiting local spatial smoothness of the voltage signal, we parameterize the voltage signal using a set of local basis functions, which reduces the dimensionality of the problem and allows us to approximately invert the microscope model. This step leverages spatial but not temporal smoothness of the underlying signal and yields noisy 3D estimates. In step two, we train a lightweight self-supervised neural network to perform spatiotemporal denoising of the inferred voltages. On simulated data, we find that DENDRO is able to recover voltages at high accuracy across an entire dendritic tree. On real voltage movies from hippocampal slices, DENDRO recovers known dendritic phenomena at single trial resolution and millisecond time-scales, and allows visualization of backpropagating action potentials in 3D.

neuroscience↗

bioRxiv2023

maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data

A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.

neuroscience↗

bioRxiv2021

Directional Gaussian Mixture Models of the gut microbiome elucidate microbial spatial structure

The gut microbiome is spatially heterogeneous, with environmental niches contributing to the distribution and composition of microbial populations. A recently developed mapping technology, MaPS-seq, aims to characterize the spatial organization of the gut microbiome by providing data about local microbial populations. However, information about the global arrangement of these populations is lost by MaPS-seq. To address this, we propose a class of Gaussian Mixture Models (GMM) with spatial dependencies between mixture components in order to computationally recover the relative spatial arrangement of microbial communities. We demonstrate on synthetic data that our spatial models can identify global spatial dynamics, accurately cluster data, and improve parameter inference over a naive GMM. We applied our model to three MaPS-Seq datasets taken from varying regions of the mouse intestine. On cecal and distal colon datasets, we find our model accurately recapitulates known spatial behaviors of the gut microbiome, including compositional differences between mucus and lumen-associated populations. Our model also seem to capture the role of a pH gradient on microbial populations in the mouse ileum and proposes new behaviors as well. ImportanceThe spatial arrangement of the microbes in the gut microbiome is a defining characteristic of its behavior. Various experimental studies have attempted to provide glimpses into the mechanisms that contribute to microbial arrangements. However, many of these descriptions are qualitative. We developed a computational method that takes microbial spatial data and learns many of the experimentally validated spatial factors. We can then use our model to propose previously unknown spatial behaviors. Our results demonstrate that the gut microbiome, while exceptionally large, has predictable spatial patterns that can be used to help us understand its role in health and disease. Code availabilitygithub.com/amepas/Spatial_Mbiome

bioinformatics↗

bioRxiv2020

Efficient and accurate inference of microbial trajectories from longitudinal count data

The recently completed second phase of the Human Microbiome Project has highlighted the relationship between dynamic changes in the microbiome and disease, motivating new microbiome study designs based on longitudinal sampling. Yet, analysis of such data is hindered by presence of technical noise, high dimensionality, and data sparsity. To address these challenges, we propose LUMINATE (LongitUdinal Microbiome INference And zero deTEction), a fast and accurate method for inferring relative abundances from noisy read count data. We demonstrate on synthetic data that LUMINATE is orders of magnitude faster than current approaches, with better or similar accuracy. This translates to feasibility of analyzing data at the requisite dimensionality for current studies. We further show that LUMINATE can accurately distinguish biological zeros, when a taxon is absent from the community, from technical zeros, when a taxon is below the detection threshold. We conclude by demonstrating the utility of LUMINATE for downstream analysis by using estimates of latent relative abundances to fit the parameters of a dynamical system, leading to more accurate predictions of community dynamics. Code availabilityhttps://github.com/tyjo/luminate

bioinformatics↗